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Skills Self-Assessment

Role: Analytics Engineer

Assessed: 0 Total: 0

AI Coding Assistants 1

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Uses GitHub Copilot to accelerate writing basic SQL queries and dbt models. Accepts suggestions for typical staging transformations: column renaming, type casting, basic JOINs. Verifies generated SQL before committing.

Effectively applies Copilot for writing dbt models and tests: prompts context through comments, uses inline suggestions for window functions and CTEs. Generates YAML documentation and schema tests with AI auto-completion.

Optimizes Copilot workflow for analytics tasks: generating complex SQL transformations, dbt macros, Jinja templates. Configures Copilot workspace with dbt project context for more accurate suggestions. Combines with ChatGPT for model design.

Defines standards for using AI tools in the analytics engineering team: guidelines for Copilot in dbt projects, review processes for AI-generated SQL. Implements AI-assisted documentation and testing of analytical models.

API Management 1

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Reads external API documentation for configuring data extraction. Documents API source parameters and limits in the project README. Describes the structure of received data for staging models.

Creates detailed API integration documentation: mapping API fields to staging model columns, describing transformations, rate limits, and retry strategies. Maintains a changelog of API changes and their impact on dbt models.

Architects the data API documentation system for the analytics platform: OpenAPI specifications for internal endpoints, auto-generation of documentation from dbt artifacts. Implements standards for describing data semantics in APIs.

Defines integration documentation standards: templates for API source descriptions, mandatory fields in data contracts, automated validation of documentation matching actual schemas.

Batch Processing 3

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Understands dbt project structure, models, and ref/source functions. Follows established patterns for writing SQL transformations and schema tests. Uses dbt run and dbt test commands following team CI/CD workflows.

Independently builds dbt transformation pipelines with incremental models, snapshots, and custom macros. Implements data quality tests with dbt-expectations and dbt-utils packages. Configures materializations and optimizes model performance.

Architects data systems with dbt. Optimizes for big data. Implements data governance and quality frameworks.

Defines the organization's dbt project architecture: modular package structure, cross-project references, shared macros library. Implements standards for incremental models, snapshot strategies, and dbt environment management (dev/staging/prod).

Uses pandas for simple data preparation tasks: reading CSV/Excel, basic filtering and aggregation for ad-hoc analytics. Understands DataFrame operations for exploring data before creating dbt models.

Applies pandas/polars for complex data preprocessing: merging heterogeneous sources, pivot tables, time series processing. Uses polars to accelerate local processing of large files before loading into the warehouse.

Architects Python pipelines for data that is difficult to process with pure SQL: NLP text processing, geocoding, complex regex parsing. Optimizes pandas/polars for processing millions of rows: chunked reading, lazy evaluation in polars.

Defines standards for Python vs SQL usage on the analytics platform: when pandas/polars is justified over dbt, templates for Python models in dbt. Implements best practices for reproducible data preparation notebooks.

Writes basic SQL transformations in dbt: SELECT with column renaming, type casting, simple filters for staging models. Understands the ELT concept and the role of SQL as the primary language for analytical transformations.

Develops complex SQL transformations in dbt: window functions for metric calculation, CTE chains for multi-step business logic, Jinja macros for DRY approach. Implements incremental models with merge strategy for optimization.

Architects optimal SQL transformations for the analytical warehouse: decomposing complex logic into intermediate models, warehouse-specific optimizations (Snowflake QUALIFY, BigQuery STRUCT). Creates reusable dbt macros for common patterns.

Defines organizational SQL transformation standards: coding style guide, mandatory patterns (surrogate keys, audit columns), dbt macros and packages library. Implements automated SQL review and performance benchmarking for critical models.

CI/CD 1

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Runs dbt commands through GitHub Actions: dbt run, dbt test in pull requests. Understands basic workflow file structure and reads CI logs for debugging model errors.

Configures CI/CD for dbt projects: sqlfluff lint on PR, slim CI with dbt run --select state:modified+, automated dbt tests. Configures dbt package and target caching for pipeline acceleration.

Architects the full CI/CD pipeline for the analytics platform: staging deploy on PR, production deploy on merge, automated regression tests. Configures dbt Cloud integration or custom GitHub Actions for blue-green model deployment.

Defines CI/CD standards for organizational analytics projects: reusable workflows for dbt, automated data quality gates, cost estimation in PRs. Implements a GitOps approach for managing warehouse resources and permissions.

Clean Code & Refactoring 1

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Understands basic code quality principles for SQL and dbt model development. Follows team SQL style guide and dbt project conventions. Writes simple, readable data transformation queries with proper naming. Accepts code review feedback on query organization and documentation.

Independently applies code quality practices in analytics engineering. Writes well-structured dbt models with clear naming and documentation. Understands trade-offs between query performance and code readability in SQL. Reviews data transformation code for correctness and maintainability.

Designs code quality standards for analytics codebases: dbt model organization, SQL style guides, data transformation testing patterns. Refactors complex SQL pipelines for readability and performance. Implements automated linting for dbt projects and warehouse-specific SQL dialects.

Establishes dbt project quality standards: sqlfluff for SQL linting, pre-commit hooks for YAML validation, mandatory dbt tests in CI/CD. Implements code review processes focused on model performance and readability.

Collaboration Tools 1

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Documents dbt models in YAML: model and column descriptions, tags for categorization. Follows team templates for doc blocks. Maintains README with dbt project structure description and instructions for new team members.

Creates comprehensive dbt project documentation: doc blocks for reusable descriptions, markdown files for business context, ERD diagrams for data models. Configures dbt docs generate and deploy for automated documentation publishing.

Architects the analytics platform documentation system: integrating dbt docs with the data catalog, automated freshness and lineage documentation, business term glossary. Implements documentation-as-code with CI validation for description completeness.

Defines documentation standards for the analytics organization: mandatory descriptions for all production models, templates for new models and packages. Implements metrics and SLA for documentation coverage, automatic reminders for undocumented models.

Data Governance 3

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Understands data catalog concepts and metadata management basics. Registers dbt models and sources in the catalog following team conventions. Uses catalog search to discover existing datasets before building new transformations.

Independently maintains data catalog entries for transformation layer. Configures automated metadata extraction from dbt docs and lineage graphs. Implements tagging taxonomies and data classification for governed self-service access.

Architects data systems with Data Catalog. Optimizes for big data. Implements data governance and quality frameworks.

Implements a data catalog for the analytics platform: integrating dbt docs with an enterprise catalog (Atlan, DataHub, Alation), automated metadata enrichment. Defines standards for classifying and tagging analytical models.

Understands data contract concepts and schema validation basics. Follows established contract specifications when building dbt models. Uses contract-defined schemas to validate transformation outputs against expected structures.

Independently defines data contracts for transformation layer outputs using dbt contracts and schema tests. Implements automated contract validation in CI/CD pipelines. Negotiates contract changes with upstream data producers.

Architects data systems with Data Contracts. Optimizes for big data. Implements data governance and quality frameworks.

Implements data contracts on the analytics platform: schema contracts in dbt, SLA for freshness and quality between source teams and analytics engineering. Defines processes for contract change management.

Understands data lineage concepts and how transformations connect sources to outputs. Follows team conventions for documenting lineage in dbt models. Uses lineage graphs in dbt docs to trace data flow through transformation layers.

Independently implements data lineage tracking across dbt transformation layer. Configures column-level lineage with dbt metadata and external lineage tools (OpenLineage, DataHub). Automates impact analysis for schema changes.

Architects data systems with Data Lineage. Optimizes for big data. Implements data governance and quality frameworks.

Implements data lineage for the analytics platform: dbt lineage graph, column-level lineage, BI integration for end-to-end tracking. Uses lineage for impact analysis when changing models and during migrations.

Data Lakehouse 1

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Understands data lake zone architecture (raw, curated, consumption). Queries data from curated layers using SQL and dbt models. Follows team conventions for partitioning, file formats, and naming standards.

Independently builds analytics data products on top of data lake layers using dbt and Spark SQL. Optimizes query performance through intelligent partitioning and Z-ordering. Ensures data quality with Great Expectations checks at zone boundaries.

Architects data systems with Data Lake Architecture. Optimizes for big data. Implements data governance and quality frameworks.

Defines the data lake architecture for the analytics platform: medallion approach (bronze/silver/gold), storage format selection (Parquet, Delta, Iceberg). Implements partitioning and retention standards for cost optimization.

Data Modeling 2

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Understands basic data modeling concepts: fact and dimension tables, star schema. Creates simple dbt staging models from team templates. Documents models in YAML files with column descriptions.

Designs dbt models by layer: staging for raw data cleansing, intermediate for business logic, marts for consumers. Applies dimensional modeling (Kimball) for analytical marts. Implements SCD Type 2 for historical dimensions.

Designs complex analytical models: wide tables for BI tools, activity schema for event analytics, bridge tables for many-to-many relationships. Optimizes the dbt DAG: eliminates circular dependencies, minimizes compute through proper layering.

Defines data modeling standards for the organization: naming conventions, dbt project layering (staging/intermediate/marts), mandatory tests and documentation. Implements data mesh approaches with domain-oriented models.

Data Orchestration 2

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Understands basic Airflow concepts: DAGs, operators, and task dependencies. Follows established DAG templates to build simple transformation pipelines. Uses dbt + Airflow integration patterns defined by the team.

Independently builds Airflow DAGs for ELT pipelines with dbt operators and data quality checks. Configures retry policies, SLAs, and alerting for transformation jobs. Optimizes task parallelism and resource pools.

Architects data systems with Apache Airflow. Optimizes for big data. Implements data governance and quality frameworks.

Defines orchestration strategy for the analytics pipeline: Airflow for coordinating dbt runs, sensors for upstream data dependencies. Implements DAG design standards: idempotency, retry policies, SLA monitoring for analytical models.

Data Quality 1

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Runs basic dbt tests: not_null, unique, accepted_values, relationships. Understands test results and fixes simple data quality issues. Monitors dbt test warnings in CI.

Configures comprehensive dbt testing: custom generic tests, dbt expectations package for statistical checks, freshness tests for sources. Implements data quality dashboards for monitoring quality metrics.

Architects the data quality strategy for the analytics platform: multi-layer testing (source → staging → marts), anomaly detection via dbt + elementary, automated alerting. Integrates quality checks into the CI/CD pipeline.

Defines the organization's data quality standards: quality SLA per layer, mandatory tests for production models, processes for responding to quality incidents. Implements a data observability platform (Monte Carlo, Elementary).

Data Visualization 1

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Creates simple dashboards in Metabase/Looker/Tableau based on prepared dbt models. Understands data visualization principles: chart type selection, filters, drill-down. Works with the mart layer as the primary source for BI.

Designs analytical dashboards with correct business logic: metric calculation at the BI vs dbt level, parameterized reports, cross-filtering. Optimizes dashboard performance through proper data modeling in the mart layer.

Defines BI development standards: semantic layer / LookML / Tableau data models for metric consistency, dashboard templates for typical business tasks. Optimizes dbt model and BI interaction through extract-based or live connection approaches.

Defines the organization's BI strategy: tool selection and standardization (Looker vs Tableau vs Metabase), governance for metrics and dashboards, self-service analytics for business users. Implements a semantic layer for unified metric definitions.

Data Warehousing 1

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Builds basic dbt models on top of existing warehouse schemas. Creates simple staging and mart layers following established dimensional modeling conventions. Understands star schema fundamentals and can implement straightforward fact and dimension tables for analytics-ready data marts.

Designs dimensional models and semantic layers that serve multiple downstream consumers. Builds reusable dbt packages with proper materialization strategies, incremental models, and well-documented data marts. Implements slowly changing dimensions and manages schema evolution without breaking existing analytics pipelines.

Architects data systems with Data Warehouse Design. Optimizes for big data. Implements data governance and quality frameworks.

Defines the analytics warehouse architecture: Kimball vs Data Vault vs One Big Table for different domains, clustering and partitioning strategy. Implements Snowflake/BigQuery best practice standards for dbt projects.

Database Optimization 2

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Understands indexing principles and their impact on analytical query speed. Reads query execution plans and identifies when indexes are used. Creates simple B-tree indexes based on senior colleagues' recommendations.

Analyzes query execution plans to determine necessary indexes in data sources. Creates composite indexes for typical analytical patterns: date filtering + dimension. Understands the trade-off between read and write speed.

Optimizes indexing at the analytical warehouse level: clustering keys in Snowflake, partition pruning in BigQuery, sort keys in Redshift. Architects indexing strategy for accelerating typical BI queries.

Defines indexing standards for the analytics platform. Implements automated query performance monitoring and optimization recommendations. Trains the team on reading query plans in the warehouse context.

Writes readable SQL queries, avoiding SELECT * and unnecessary JOINs. Uses EXPLAIN to understand basic execution plans. Follows team recommendations for writing efficient dbt models.

Optimizes dbt models and SQL queries: rewrites subqueries as CTEs, eliminates redundant JOINs, uses incremental strategies for heavy models. Analyzes query profiles in Snowflake/BigQuery to identify bottlenecks.

Performs deep optimization of analytical queries: refactoring the dbt DAG to reduce compute costs, configuring warehouse-specific optimizations (Snowflake warehouse sizing, BigQuery slot allocation). Implements query tagging for cost attribution.

Defines performance standards for analytical models: SLA on execution time, compute budgets. Implements automated performance testing for dbt models and alerting on query speed degradation.

Logging 1

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Understands dbt run logs: interprets warnings and errors during model execution. Uses dbt log output for diagnosing SQL transformation errors. Monitors test results in the CI/CD pipeline.

Configures structured logging for the analytics pipeline: dbt artifacts (manifest.json, run_results.json) for monitoring, custom logging in Python scripts for data extraction. Creates alerts based on dbt test results.

Architects observability for the analytics platform: dbt Cloud/Elementary for model monitoring, structured logging for custom extractors, alerting on data anomalies. Integrates dbt run metadata with Datadog/Grafana.

Defines observability standards for the analytics platform: dashboards for dbt pipeline health, SLA monitoring for data freshness and quality. Implements incident management processes for data quality issues with structured runbooks.

NoSQL Databases 1

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Performs basic analytical queries on ClickHouse: aggregations, filtering by time ranges, simple GROUP BY. Understands columnar storage and its advantages for analytical tasks. Uses HTTP interface or client.

Writes complex analytical queries using ClickHouse-specific functions: arrayJoin, windowFunnel, retention. Optimizes queries through proper ORDER BY key selection and PREWHERE usage for filtering.

Designs analytical tables in ClickHouse: engine selection (MergeTree, AggregatingMergeTree), partition keys for time-series data. Configures materialized views for real-time metric aggregations.

Defines the ClickHouse usage strategy within the analytics platform: as an engine for real-time dashboards, integration with dbt via the ClickHouse adapter. Implements modeling standards and TTL policies for storage management.

Relational Databases 1

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Writes basic SELECT queries on PostgreSQL for extracting data from staging tables. Understands data types, JOINs, and GROUP BY for simple analytical tasks. Uses pgAdmin or DBeaver for exploring database structure.

Creates complex analytical queries with CTEs, window functions, and subqueries in PostgreSQL. Uses EXPLAIN ANALYZE for profiling queries on large tables. Works with PostgreSQL-specific types: JSONB, ARRAY, INTERVAL.

Optimizes PostgreSQL as a data source for the analytics platform: configures logical replication for CDC, partitioning for historical data. Designs schemas for efficient data extraction into dbt models.

Defines the PostgreSQL usage strategy in the analytics stack: read replicas for BI queries, materialized views for intermediate aggregations. Implements naming and schema documentation standards for the data catalog.

Web Frameworks 1

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Additional skills (optional) 28

These skills do not affect the main grade but show the breadth of your profile.

AI Coding Assistants

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Getting started with LLM assistants to accelerate analytics engineering work. Understands how to use ChatGPT and Claude for generating SQL queries, writing dbt models, and data analysis.

Effectively uses LLMs for analytics engineering tasks — SQL optimization, dbt test generation, and model documentation. Applies AI assistants for data exploration and prototyping analytics pipelines.

Integrates LLM tools into analytics team workflows for automating routine tasks. Creates prompt templates for generating data quality checks, analyzing data lineage, and optimizing query performance.

Develops the strategy for using AI tools in the analytics team, standardizes approaches to LLM-assisted development. Trains engineers on effectively applying ChatGPT and Claude for improved data work productivity.

Getting started with Cursor IDE's AI capabilities for improving analytics development productivity. Understands SQL auto-completion, dbt model generation, and contextual search for data projects.

Effectively uses Cursor IDE to accelerate analytics development — generating SQL queries, dbt tests, and documentation. Configures AI context for proper operation with dbt projects, Jinja templates, and analytics patterns.

Optimizes analytics development workflows through Cursor IDE — multi-file editing of dbt models, custom rules for SQL style. Creates templates for typical analytics tasks — staging, intermediate, and mart models.

Establishes Cursor IDE usage standards for the analytics team, configures shared settings for dbt projects. Trains engineers on effectively using AI tools to improve quality and speed of analytics development.

Algorithms & Data Structures

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Understands basic algorithmic concepts for analytics: simple SQL aggregation operations, basic sorting and grouping strategies, data deduplication approaches. Follows team guidance on query optimization and transformation algorithm selection.

Independently applies algorithmic thinking in analytics: evaluates query execution plan efficiency, selects appropriate aggregation strategies for large datasets, understands partitioning algorithm impact on performance. Analyzes complexity of data transformation operations.

Applies algorithmic thinking to analytics pipeline optimization: query execution plan analysis for complex aggregations, incremental computation algorithms for large dataset processing, partitioning strategies based on data distribution patterns. Designs efficient data transformation algorithms for warehouse workloads.

Evaluates computational complexity of SQL transformations and dbt models on large data volumes. Optimizes aggregation algorithms and window functions in analytical queries. Implements query plan profiling practices in the data warehouse.

Understands basic data structures for analytics engineering: SQL table schemas, dbt model source/ref relationships, staging layer data formats. Follows team conventions for organizing dbt project structure and data transformation queries.

Independently selects appropriate data structures for analytics: dimensional modeling for warehouse schemas, CTEs for complex SQL transformations, staging/mart layer organization in dbt. Understands trade-offs between normalization levels for analytical query performance.

Selects optimal data structures for analytics platforms: materialized views, pre-aggregation tables, columnar partitioning strategies for warehouse queries. Optimizes dbt model DAG structure for incremental processing. Designs dimension/fact table schemas for query performance across large analytical workloads.

Defines data modeling standards in dbt: choosing between nested structures and normalized tables, ARRAY/MAP types in analytical queries. Reviews data structure decisions in the context of BI dashboard performance.

Application Security

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Getting familiar with major OWASP vulnerabilities and their impact on analytics system security. Understands SQL injection risks and unauthorized access to analytical data and dashboards.

Applies OWASP recommendations to protect analytics pipelines and dashboards from common vulnerabilities. Implements input validation, parameterized queries, and proper access management for analytics resources.

Integrates OWASP practices into the analytics system lifecycle — data warehouse protection, auditing access to sensitive data. Architects multi-layered analytics platform security with PII masking and column-level security.

Establishes the analytics platform security policy based on OWASP recommendations. Coordinates security reviews of analytics pipelines and ensures data protection compliance across all processing stages.

Getting familiar with secure coding principles for developing analytical pipelines. Understands risks of storing credentials in code, the need for SQL parameterization, and basic protection of sensitive data.

Applies secure coding practices in analytics pipelines — managing secrets through vault, parameterized queries, and PII masking. Implements audit logging for tracking access to sensitive data.

Architects secure analytics systems with data encryption in transit and at rest, column-level masking, and row-level security. Implements automated security checks in CI/CD for dbt projects and ETL pipelines.

Establishes secure coding standards for the analytics team, including data classification and access control policies. Organizes security reviews of analytics pipelines and trains developers on defensive data engineering.

Caching

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Uses Redis at a basic level in dbt. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with Redis in dbt. Understands internals and optimizes performance. Writes tests using dbt test.

Uses Redis for caching heavy analytical query results and lookup tables. Implements TTL strategies to ensure freshness of cached metrics in BI dashboards. Configures invalidation on dbt model updates.

Defines the analytics platform caching strategy: Redis for hot metrics and KPIs, invalidation policies on model recalculation. Implements caching at the semantic layer for accelerating BI queries.

Cloud Providers

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Works with basic AWS services for analytics: S3 for raw data storage, Redshift/Athena for SQL queries. Understands IAM roles for data access and basic data lake structure.

Configures AWS infrastructure for analytics tasks: S3 buckets with lifecycle policies for raw/processed data, Glue for cataloging, Athena for ad-hoc analytics. Integrates dbt with Redshift/Athena via profiles.

Architects the AWS analytics platform: Redshift Serverless for cost optimization, Lake Formation for data governance, Step Functions for ELT orchestration. Configures cross-account data access for different teams.

Defines the AWS strategy for organizational analytics: choosing between Redshift and Athena for different workloads, cost management through reserved capacity and serverless. Implements tagging and cost allocation for analytics resources.

Code Review

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Participates in code review of dbt models: checks naming conventions, presence of tests and documentation. Leaves constructive comments on SQL style and YAML configuration structure. Learns from senior colleagues' feedback.

Conducts quality code reviews of dbt models: verifies transformation business logic, correctness of JOINs and window functions, SQL performance. Validates incremental logic and snapshot configurations. Pays attention to edge cases.

Conducts architectural reviews of dbt projects: evaluates model design, correct layering (staging/intermediate/marts), reusability. Reviews impact analysis for changes to shared models. Mentors through detailed review explanations.

Defines code review standards for analytics engineering: checklists for dbt PRs (model, tests, docs, performance), automated checks in CI, review SLA. Builds a culture of quality through constructive feedback and knowledge sharing.

Concurrency & Parallelism

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Studies asynchronous programming basics for parallel loading and processing of analytical data. Understands async/await concepts, promises, and their application in ETL scripts and data pipelines.

Implements asynchronous data processing in analytics pipelines — parallel loading from multiple sources and concurrent transformations. Applies async patterns to optimize ETL task execution speed.

Architects high-performance analytics pipelines with asynchronous orchestration, backpressure, and proper error handling. Optimizes parallel data loading from multiple API sources with rate limiting and retry strategies.

Defines asynchronous data processing standards for the analytics platform, including concurrency patterns and error handling. Trains the team on effectively using async patterns for accelerating analytics pipelines.

Studies multithreading and parallel computing basics for accelerating analytical data processing. Understands thread, process, and basic synchronization primitive concepts when working with large datasets.

Implements parallel data processing in analytics pipelines — concurrent loading from sources and parallel transformations. Applies multiprocessing for accelerating Python ETL and understands GIL limitations.

Architects high-performance analytics pipelines with parallel execution, partitioned processing, and lock-free algorithms. Optimizes resource utilization during mass data processing with concurrent access control.

Defines parallel data processing standards for the analytics platform, including partitioning strategies and error handling. Trains the team on safe parallel programming to accelerate analytical tasks.

Containerization

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Uses Docker to run local development environments: dbt connected to dev warehouse, PostgreSQL for testing. Understands Dockerfile and docker-compose for the analytics stack. Works with pre-built dbt images.

Creates Docker images for analytics tools: dbt with custom packages, Python scripts for data extraction, sqlfluff for CI. Configures docker-compose for local testing of the full analytics pipeline.

Architects a containerized environment for the analytics platform: multi-stage builds for dbt projects, image optimization for CI/CD, dbt package caching. Configures containerization for custom data connectors and quality tools.

Defines containerization standards for the analytics stack: base images for dbt projects, templates for data extraction jobs, security policies for images. Implements a container registry and versioning for analytics tools.

Distributed Tracing

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Studies OpenTelemetry basics for monitoring analytical pipelines and ETL processes. Understands trace and metric concepts for tracking analytics task execution and diagnosing issues.

Instruments analytics pipelines with OpenTelemetry for tracing ETL processes from source to warehouse. Configures metrics for task duration, data volumes, and error rates for analytics services.

Architects the analytics platform observability system based on OpenTelemetry with ETL trace and data quality metric correlation. Implements custom spans for each transformation stage and automated anomaly detection in pipelines.

Defines instrumentation standards for analytics services via OpenTelemetry, including semantic conventions for data operations. Implements an observability-driven approach to ETL process optimization and data quality monitoring.

Git & Workflows

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Uses Git for dbt project work: cloning, creating feature branches, basic commits. Understands gitflow for dbt: feature branch → pull request → merge to main. Works with .gitignore for target/ and dbt_packages/.

Effectively uses Git in dbt development: atomic commits per model/model group, rebase for clean history, cherry-pick for production hotfixes. Resolves merge conflicts in SQL models and YAML configurations.

Architects Git workflows for the analytics team: branching strategy for dbt (trunk-based vs gitflow), protected branches with required CI checks. Configures Git hooks for sqlfluff linting and dbt compile validation before commit.

Defines version control standards for the analytics organization: mono-repo vs multi-repo for dbt projects, semantic versioning for dbt packages, release management. Implements Git-based governance for production models.

GraphQL

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Studies GraphQL basics and its application for creating analytics APIs. Understands concepts of queries, mutations, and schema-first approach for providing analytical data to consumers.

Implements GraphQL APIs for analytics data access with flexible filtering and aggregation. Designs schemas for analytical entities with pagination, nested resolvers, and N+1 query optimization against the warehouse.

Architects a GraphQL semantic layer for the analytics platform with caching, rate limiting, and field-level authorization. Implements subscriptions for real-time analytics and integrates GraphQL with BI tools for self-service.

Defines GraphQL API standards for the analytics platform, including naming conventions and metric/dimension patterns. Coordinates development of a unified data API for providing analytical data to all organizational consumers.

Integration Testing

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Understands basics of integration testing for data pipeline stages. Writes simple tests verifying data transformations between source systems and warehouse tables. Follows team testing guidelines for ETL/ELT job validation.

Conducts integration tests of dbt models: verifying connectivity between layers (staging → intermediate → marts), validating referential integrity, testing full data pass through the pipeline.

Architects analytics platform integration tests: end-to-end verification from sources to BI dashboards, data reconciliation between warehouse layers. Automates dbt testing with real data in the staging environment.

Defines integration testing standards: dbt source freshness checks, cross-model consistency tests, automated BI dashboard validation. Implements contract testing between data producers and analytical models.

Kubernetes & Orchestration

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Studies basic Kubernetes concepts to understand analytics service infrastructure. Understands the role of container orchestration for deploying Airflow, dbt, and analytics APIs in production.

Deploys analytics services in Kubernetes — Airflow workers, dbt jobs, and APIs for analytics data access. Configures CronJobs for regular ETL tasks and resource limits for optimal cluster utilization.

Architects Kubernetes infrastructure for the analytics platform with autoscaling Spark/Airflow workers and batch job management. Implements task prioritization, spot instances for cost savings, and workload isolation for data quality.

Defines the analytics platform orchestration strategy in Kubernetes, standardizes analytics service deployment. Implements GitOps for infrastructure management and ensures ETL process reliability in containerized environments.

Message Queues & Event Streaming

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Uses Apache Kafka at a basic level in dbt. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with Apache Kafka in dbt. Understands internals and optimizes performance. Writes tests using dbt test.

Integrates Kafka streams as data sources for analytical models. Configures consumer applications for capturing events into the warehouse staging layer. Works with Schema Registry for ensuring schema compatibility in downstream models.

Defines the strategy for integrating streaming data into the analytics platform: Kafka topics as sources for near-real-time dashboards, CDC streams for incremental models, schema evolution policies for analytics contracts.

Metrics & Monitoring

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Studies monitoring basics with Prometheus and Grafana for tracking analytical pipeline health. Understands metric and alert concepts as applied to ETL processes and data warehouses.

Configures analytics pipeline monitoring in Prometheus — ETL duration metrics, processed data volumes, and load errors. Creates Grafana dashboards for tracking data freshness and analytics platform SLA.

Architects a comprehensive analytics platform monitoring system with data quality, freshness, and query cost metrics. Implements alerts for early detection of pipeline issues and warehouse performance degradation.

Defines the analytics platform monitoring strategy, standardizes data quality and performance metrics. Implements a data-driven approach to ETL process optimization based on Prometheus and Grafana monitoring.

Networking

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Studies networking protocol basics needed for analytics systems — HTTP, TCP, DNS. Understands basic networking principles between ETL tools and data sources.

Applies networking knowledge to configure reliable analytics pipeline interaction with data sources. Understands firewall rules, VPN tunnels, and SSH connections for secure access to database sources.

Architects the analytics platform network with optimization for bulk data loading and cloud storage interaction. Configures Private Link, VPC peering, and optimal routing to minimize ETL latency.

Defines network standards for the analytics platform, including secure source connections and data environment isolation. Coordinates network architecture with infrastructure teams to ensure required throughput.

OOP & Design Patterns

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Getting familiar with main design patterns applicable to analytics engineering — Factory, Strategy, Observer. Understands how patterns help organize data pipeline and transformation code.

Applies design patterns in analytics systems — Strategy for transformation selection, Factory for connector creation, Pipeline for processing chains. Structures dbt project code using template patterns.

Architects analytics platforms using architectural patterns — Mediator for orchestration, Repository for storage abstraction. Creates extensible frameworks for typical analytics tasks with plug-in architecture.

Defines a design pattern catalog for the analytics platform, trains the team on applying them in data engineering. Establishes best practices for using patterns to build maintainable and testable analytics systems.

Getting familiar with object-oriented programming basics for structuring analytical code. Understands encapsulation, inheritance, and polymorphism concepts as applied to organizing data models and transformations.

Applies OOP principles for creating reusable analytics components — transformer classes, validators, and data connectors. Uses SOLID principles for organizing dbt model code and analytics pipelines.

Architects object-oriented frameworks for analytics pipelines with abstract transformation classes and loading strategies. Creates extensible libraries for typical analytics operations with polymorphic behavior.

Defines standards for applying OOP principles in analytics development, ensuring code modularity and testability. Establishes architectural patterns for the analytics engineering team balancing simplicity and extensibility.

Prompt Engineering

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Formulates basic prompts for ChatGPT/Claude when writing SQL queries and dbt models. Describes the data schema and desired result to get correct SQL. Uses AI to understand unfamiliar SQL patterns.

Composes effective prompts for analytics tasks: describing data model context, few-shot examples for the team's SQL style. Uses AI for refactoring dbt models, writing tests, and generating model documentation.

Develops prompt templates for typical analytics engineering tasks: data modeling design, SQL optimization, test generation. Creates system prompts with dbt project context, naming conventions, and team style for consistent AI output.

Defines the prompt engineering strategy for the analytics engineering team: a prompt library for modeling, testing, and documentation. Implements AI-powered code review for dbt models with custom prompts for best practice verification.

REST API

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Understands REST API principles for integrating with external data sources. Uses Python requests/httpx for extracting data from APIs into staging models. Understands HTTP methods, status codes, and pagination.

Designs reliable pipelines for extracting data from REST APIs: handling pagination, rate limiting, retry logic. Creates dbt sources from API data via intermediate staging tables. Documents API dependencies in the data catalog.

Develops a reusable framework for analytics platform API integrations: generic extractors configured via YAML, schema inference, automatic data type mapping. Designs error handling and alerting for API sources.

Defines API integration standards for the analytics stack: choosing between custom extractors and managed tools (Fivetran, Airbyte), SLA for API data freshness. Implements data contracts for internal APIs as analytics sources.

Search Engines

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Uses Elasticsearch / OpenSearch at a basic level in dbt. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with Elasticsearch / OpenSearch in dbt. Understands internals and optimizes performance. Writes tests using dbt test.

Configures Elasticsearch as a search engine for the data catalog and discovery platform. Indexes dbt model metadata, table and column descriptions for full-text search by analysts.

Defines the search functionality architecture for the analytics platform: Elasticsearch for data discovery, autocomplete for metric and model names, fuzzy search by descriptions and tags in the data catalog.

System Design

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Studies basic system design principles applied to analytical platforms. Understands scalability, partitioning concepts, and differences between OLTP and OLAP architectures for analytical systems.

Architects analytics pipeline considering data volumes, update frequency, and freshness requirements. Selects patterns for data warehouse organization — star schema, medallion architecture, ELT vs ETL.

Architects the analytics platform with real-time and batch layers, data mesh approach, and federation queries. Designs scalable systems considering the CAP theorem and eventual consistency for analytical data.

Defines architectural principles for the analytics platform, including data lakehouse, streaming, and semantic layer. Leads system design to enable business user self-service and analytics scalability.

Type Systems

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Studies typing basics and their importance for analytics code reliability. Understands the difference between typed and untyped approaches, uses type annotations in Python for data pipelines.

Applies strict typing in analytics pipelines through Pydantic, dataclasses, and typed DataFrames. Uses schema validation for data contracts between sources, transformations, and consumers.

Architects type-safe analytics systems with runtime validation through Great Expectations and typed data contracts. Implements data contracts between teams with automated schema evolution and compatibility checking.

Defines typing standards for the analytics platform, including data contracts and schema governance. Ensures type consistency between ETL pipelines, warehouse, and analytics APIs for data reliability.

Unit Testing

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Understands basic unit testing for analytics code: dbt test fundamentals, simple data quality assertions, schema test setup. Follows team practices for testing SQL transformations and data model constraints.

Writes dbt unit tests to verify transformation business logic: fixed input data, expected results. Tests edge cases in SQL models: NULL handling, boundary dates, division by zero in metrics.

Architects the unit testing strategy for dbt projects: dbt unit tests for critical transformations, pytest for custom Python extractors. Implements test fixtures and factories for generating test data covering real analytical scenarios.

Defines analytical model testing standards: mandatory test coverage for the mart layer, automated regression suites for KPI metrics. Implements test environments with representative data samples for model validation.