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Skills Self-Assessment

Role: Analytics Engineer

Assessed: 0 Total: 0

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.

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

Architects the unified data documentation platform: integrating dbt docs, API catalog, and BI glossary into a single data portal. Defines the data literacy and self-service documentation access strategy for business users.

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.

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).

Architects the enterprise dbt platform evolution strategy: multi-project mono-repo vs multi-repo, dbt mesh for cross-team dependencies, migration to dbt Cloud. Defines the roadmap for MetricFlow/Semantic Layer adoption to unify business metrics.

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.

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.

Architects the transformation tool selection strategy: dbt (SQL) as primary, Python models for ML feature engineering and complex logic. Defines the integration architecture for pandas/polars/PySpark with dbt for hybrid pipelines.

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.

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.

Architects the enterprise transformation layer strategy: SQL dialect unification through dbt adapters, portable business logic between warehouses. Defines the architecture for supporting real-time and batch transformations on a unified platform.

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.

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.

Architects the enterprise data documentation platform: unified portal with dbt docs, BI glossary, and data contracts. Defines the data literacy strategy: onboarding for new analysts, self-service data discovery, automated documentation via AI.

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.

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.

Architects a unified enterprise data discovery platform: automatic lineage from sources to BI, semantic search by business terms, data marketplace for self-service. Defines governance processes for cataloging and data stewardship.

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.

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.

Architects the enterprise data contracts system: contract format standards, automated enforcement through CI/CD, integration with data mesh. Defines governance for contract evolution and breaking changes in analytical models.

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.

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.

Architects enterprise data lineage: automatic tracking from source systems through ETL/ELT to BI dashboards, cross-tool lineage (Airflow → dbt → Tableau). Defines lineage strategy for compliance (GDPR, SOX) and data governance.

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.

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.

Architects the enterprise lakehouse: Delta Lake/Iceberg as open table format, integration with dbt for transformations, unified governance. Defines the strategy for combining data lake and warehouse for different analytical workloads.

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.

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.

Architects the enterprise data modeling strategy: unified semantic layer via dbt metrics/MetricFlow, data vault for historical storage, domain-driven models. Defines the balance between normalization and denormalization for different consumers.

Understands database migration fundamentals for analytics: schema evolution in data warehouses, column additions/modifications with dbt, and basic migration scripts for staging tables. Follows team patterns for versioned schema changes and backward-compatible transforms.

Independently designs schemas and optimizes queries with Database Migrations. Understands indexing and execution plans. Uses ORM effectively.

Defines the data management strategy at the product level. Establishes database migration standards. Conducts reviews of data schemas and scaling strategies.

Defines the analytical model evolution strategy: schema change versioning through dbt, backward-compatible migrations for BI dashboards. Architects zero-downtime migration processes between warehouse platforms (Redshift→Snowflake, BigQuery).

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.

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.

Architects enterprise analytics platform orchestration: Airflow/Dagster for multi-project dbt, event-driven triggers, cross-team dependency management. Defines migration strategy to managed orchestration (dbt Cloud, Dagster Cloud).

Understands Dagster/Prefect basics for orchestrating dbt models and data transformations. Runs existing pipelines, reads logs, and troubleshoots simple task failures in analytics workflows.

Independently implements data pipelines with Dagster / Prefect. Optimizes performance. Ensures data quality.

Implements modern orchestration for analytics: Dagster assets as native integration with dbt models, software-defined assets for Python transformations. Defines standards for observable, testable pipelines with built-in data quality checks.

Architects next-gen orchestration: Dagster's asset-based approach for a unified analytics platform, integration with dbt mesh, declarative scheduling. Defines migration strategy from Airflow to asset-centric orchestration.

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.

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).

Architects the enterprise data quality strategy: unified quality framework for all sources and transformations, ML-driven anomaly detection, automated root cause analysis. Defines a quality-as-code approach with version-controlled rules.

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 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.

Architects enterprise BI: multi-tool strategy for different audiences, embedded analytics for products, real-time dashboards. Defines the roadmap from traditional BI to self-service analytics and metrics layer.

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.

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.

Architects the enterprise data warehouse strategy: multi-warehouse for different workloads (analytics, ML, reporting), cost governance through resource monitors. Defines data sharing architecture between business units and external partners.

Database Optimization 1

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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.

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.

Architects the analytics warehouse cost optimization strategy: multi-cluster configurations, auto-suspend policies, query routing. Defines architectural decisions balancing cost and analytics performance.

Message Queues & Event Streaming 1

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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.

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.

Architects real-time analytics on ClickHouse: cluster configuration, replication, distributed tables for scaling. Defines when to use ClickHouse vs warehouse (Snowflake/BigQuery) for different analytical tasks.

Search Engines 1

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Web Frameworks 1

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Additional skills (optional) 32

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.

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.

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.

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.

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.

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.

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.

Defines the analytics platform performance optimization strategy. Architects approaches for processing petabyte datasets: incremental models, approximate algorithms (HyperLogLog for distinct count), materialized views for heavy aggregations.

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.

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.

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.

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.

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.

Architects the analytics caching infrastructure: multi-tier caching (warehouse result cache, application cache, CDN for embedded dashboards). Defines cache consistency strategy for incremental data updates.

CI/CD

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Clean Code & Refactoring

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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.

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.

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.

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.

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.

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.

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.

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.

Database Optimization

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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.

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.

Architects the platform-level data access optimization strategy: automatic clustering, search optimization in Snowflake, BI Engine in BigQuery. Defines cost-based optimization approaches for analytical queries.

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.

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.

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.

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.

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.

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.

Logging

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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.

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.

Metrics & Monitoring

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Networking

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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.

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.

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.

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.

Relational Databases

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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.

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.

Architects PostgreSQL's role in the analytics platform: as an operational datastore, as a metadata backend for tools (Airflow, dbt). Defines the data migration strategy from PostgreSQL to the analytical warehouse.

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.

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.

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.

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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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.

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.