Grade Calculator — DevLevelUp

Select a role to assess

Rate your skill proficiency — the system will calculate your recommended grade

Role

Skills Self-Assessment

Role: BI Analyst

Assessed: 0 Total: 0

API Management 1

▼

Understands basics of API documentation for data source connectors and reporting service endpoints. Reads and follows existing API docs when configuring BI tool integrations. Documents simple data query parameters and response formats for dashboard data sources.

Independently creates API documentation for data warehouse query endpoints and reporting service integrations. Writes integration guides for BI tool data connector configurations with authentication examples. Documents data refresh API contracts and parametric report generation endpoints.

Defines API documentation strategy for enterprise BI and data platform integrations. Establishes design standards for data access API documentation including data governance annotations and usage guidelines. Coordinates cross-team documentation reviews ensuring data API consistency across analytics platforms.

Defines organizational API documentation strategy for enterprise data and analytics platforms. Designs platform-level documentation architecture for data access APIs with integrated governance metadata and usage analytics. Establishes enterprise API documentation standards balancing self-service accessibility with data governance compliance.

Batch Processing 3

▼

Understands dbt basics and how it transforms raw data into BI-ready datasets. Follows team conventions for writing simple dbt models that feed dashboards. Uses dbt docs to understand data lineage and model dependencies.

Independently writes dbt models for BI reporting layer: metric definitions, aggregate tables, and dimensional models. Implements schema tests and data freshness checks. Configures dbt exposures to document downstream dashboard dependencies.

Defines dbt development standards for BI organization. Establishes model naming conventions, testing requirements, and code review processes. Coordinates dbt governance across teams with shared macro libraries and package management strategies.

Defines enterprise dbt strategy and transformation platform architecture. Evaluates dbt Cloud vs Core trade-offs, Mesh adoption, and semantic layer integration paths. Shapes organizational data transformation governance aligned with data mesh principles.

Understands Pandas basics for BI workflows: DataFrame creation from various sources (CSV, Excel, SQL), basic data filtering and aggregation, and pivot table operations. Cleans and prepares datasets for dashboard visualization. Follows team conventions for data transformation scripts.

Implements efficient BI data pipelines with Pandas: multi-source data merging, complex aggregation chains, and time-series analysis for trend detection. Optimizes memory usage with proper dtype selection and chunked reading for large files. Creates reusable data transformation functions for recurring analytics tasks.

Defines data engineering strategy for BI organization. Shapes data platform architecture: tool selection (Pandas vs Polars vs Spark), data pipeline standards, and data quality governance. Coordinates data teams on shared transformation libraries and best practices. Optimizes data mesh/data fabric approaches for self-service analytics.

Defines organizational data strategy for business intelligence: enterprise data platform design, data governance framework, and self-service analytics vision. Evaluates emerging data technologies for BI transformation. Drives data-driven culture adoption across the organization. Shapes data literacy and tooling standards at enterprise level.

Understands SQL-based ETL basics for BI warehouses. Writes simple extract-load queries for dimensional tables. Follows existing star schema load patterns and naming conventions for staging layers.

Builds ETL pipelines that populate dimensional models for BI reporting. Implements SCD Type 1/2 loads, manages surrogate keys, and ensures referential integrity across fact and dimension tables in the warehouse.

Defines BI warehouse ETL strategy and standards across teams. Governs dimensional modeling conventions, orchestrates cross-domain data integration, and establishes SLA-driven refresh schedules for executive dashboards.

Shapes organization-wide BI data platform vision and ETL architecture. Drives adoption of modern ELT patterns, defines enterprise semantic layer standards, and aligns warehouse ETL strategy with long-term business intelligence roadmap.

Data Governance 3

▼

Understands data catalog purpose and basic metadata navigation. Uses catalog to discover available data sources for dashboards and reports. Documents BI metric definitions and dashboard data dependencies in the catalog.

Independently manages BI layer metadata in the data catalog. Configures metric definitions, KPI hierarchies, and dashboard-to-source mappings. Implements data freshness indicators and quality badges for reporting datasets.

Defines data catalog governance strategy for BI organization. Establishes metadata quality standards, stewardship roles, and catalog adoption metrics. Drives cultural shift toward data-as-a-product through catalog-first workflows.

Defines enterprise data catalog strategy spanning all data domains. Evaluates catalog platforms (DataHub, Atlan, Alation) and shapes metadata architecture. Establishes organizational data governance framework with catalog as central pillar.

Understands data contract purpose and how contracts define data expectations for BI consumers. Follows contract specifications when connecting dashboards to data sources. Reports contract violations affecting report accuracy.

Independently manages data contracts for BI consumption layer. Defines metric contracts specifying aggregation rules, granularity, and freshness SLAs. Implements contract-based data validation before dashboard publication.

Defines data contract governance strategy for BI organization. Establishes contract lifecycle management processes, ownership models, and cross-team negotiation protocols. Measures contract adoption and violation reduction metrics.

Defines enterprise data contract strategy spanning all organizational data domains. Shapes contract specification standards and tooling decisions. Drives industry-aligned practices for data mesh contract governance.

Understands data lineage basics and how source data flows into BI dashboards. Uses lineage tools to trace data origins when debugging report discrepancies. Documents dashboard-to-source data dependencies following team guidelines.

Independently traces data lineage from BI dashboards to source systems. Uses lineage tools to perform impact analysis before data source changes. Implements lineage-based documentation for metric calculation transparency.

Defines data lineage governance strategy for BI organization. Establishes lineage completeness standards, ownership models, and integration requirements across data stack. Drives lineage-powered compliance and audit capabilities.

Defines enterprise data lineage strategy spanning all data domains and tooling layers. Evaluates lineage platforms (OpenLineage, Marquez, DataHub) and shapes metadata architecture. Drives regulatory compliance through lineage-based audit trails.

Data Modeling 1

▼

Understands data modeling basics: star and snowflake schemas, fact and dimension tables. Follows team conventions for building BI-optimized data models. Uses existing dimensional models to build accurate reports and dashboards.

Independently designs dimensional models for BI reporting. Implements slowly changing dimensions (SCD Type 1/2), conformed dimensions, and aggregate tables. Optimizes data models for dashboard query performance with proper indexing and partitioning.

Defines data modeling standards for BI organization. Establishes naming conventions, modeling guidelines, and review processes. Coordinates dimensional model governance across teams with conformance rules and shared dimension management.

Defines enterprise data modeling strategy spanning operational and analytical systems. Evaluates modeling methodologies (Kimball, Inmon, Data Vault) and shapes organizational standards. Drives data model governance framework aligned with data mesh principles.

Data Orchestration 1

▼

Understands basic Airflow DAG structure and scheduling concepts. Monitors scheduled report refresh pipelines and identifies failures. Follows team guidelines for triggering dashboard data updates through Airflow UI.

Independently configures Airflow DAGs for scheduled report generation and dashboard data refresh. Implements data quality sensors to validate source data before BI layer updates. Troubleshoots pipeline failures affecting reporting.

Defines BI data pipeline strategy and Airflow platform standards. Establishes DAG development guidelines, code review practices, and deployment workflows for reporting team. Coordinates data freshness SLAs with stakeholders.

Defines enterprise data orchestration strategy spanning Airflow, dbt, and BI tools. Evaluates orchestration platforms and migration paths. Shapes organizational data delivery standards and cross-team pipeline governance.

Data Quality 1

▼

Uses basic data quality checks in Tableau and Power BI dashboards. Validates data sources in SQL before building reports. Follows team standards for data cleansing in Excel and Google Sheets. Identifies obvious anomalies in ClickHouse/PostgreSQL query results.

Implements automated data quality checks in BI pipelines using SQL and dbt tests. Configures freshness and completeness monitors for Tableau/Power BI dashboards. Builds validation layers in ClickHouse and BigQuery to catch schema drift. Creates quality scorecards and alerting for key metrics.

Defines data quality strategy across BI and analytics teams. Coordinates data mesh principles with embedded quality gates. Shapes platform roadmap prioritizing observability, lineage, and quality automation. Drives cross-team alignment on data contracts and SLAs.

Defines enterprise data quality vision across BI, analytics, and reporting. Architects governance frameworks with automated quality enforcement for Tableau, Power BI, and cloud warehouses. Establishes standards for data certification, lineage, and trust scoring. Influences industry practices.

Data Visualization 1

▼

Builds basic dashboards in Tableau and Power BI following team templates. Understands core KPI definitions and applies standard visualization types for executive reporting. Follows BI style guides and documentation for consistent output.

Independently designs interactive dashboards in Tableau and Power BI with calculated fields and LOD expressions. Optimizes query performance for large datasets. Implements self-service BI layers enabling business users to explore KPIs autonomously.

Defines enterprise BI strategy and dashboard platform roadmap. Shapes self-service BI culture enabling business-driven analytics. Coordinates BI teams across departments and standardizes KPI definitions. Optimizes hybrid approaches combining Tableau, Power BI, and Looker ecosystems.

Defines organizational BI strategy aligning dashboard platforms with business objectives. Designs enterprise-wide self-service BI architecture spanning Tableau, Power BI, and Looker. Establishes cross-departmental KPI governance framework ensuring data-driven decision-making at executive level.

Data Warehousing 1

▼

Navigates existing star and snowflake schemas to build reports and dashboards. Understands the difference between fact and dimension tables and writes queries that correctly join them. Uses pre-built aggregate tables for dashboard performance and follows established naming conventions in the BI layer.

Designs star and snowflake schemas optimized for BI reporting workloads. Creates aggregate tables and materialized views that significantly improve dashboard query performance. Proposes schema changes to the warehouse team based on reporting requirements and collaborates on dimensional modeling decisions for new data domains.

Defines the organization-wide warehouse schema strategy for BI consumption, aligning star and snowflake designs with long-term reporting roadmaps. Establishes standards for aggregate table creation, materialized view governance, and schema versioning. Coordinates with data engineering leadership to ensure warehouse evolution supports both operational and strategic BI initiatives.

Shapes the enterprise data warehouse vision for BI, driving adoption of modern patterns like data mesh semantic layers and unified metrics platforms. Evaluates emerging warehouse technologies and their impact on BI schema design at organizational scale. Influences vendor and platform decisions, ensuring warehouse architecture enables self-service analytics while maintaining data quality and governance standards.

Database Optimization 1

▼

Understands query optimization basics for analytical work: reading execution plans, identifying full table scans, and basic index usage for WHERE/JOIN columns. Follows team guidelines on writing efficient SQL for reports and dashboards.

Independently designs and optimizes analytical queries: window functions for running calculations, CTEs for query readability and reuse, and query decomposition for complex reports. Analyzes execution plans to choose between nested loops, hash joins, and merge joins. Optimizes materialized views for dashboard performance.

Defines query optimization strategy for BI department. Establishes SQL performance standards, query review processes, and performance monitoring requirements. Conducts reviews of critical analytical queries. Creates query optimization guidelines and training programs for BI analysts.

Defines organizational data strategy for business analytics: query engine selection for different analytical workloads, data access architecture standardization, and performance governance at enterprise scale. Evaluates emerging query technologies (vectorized engines, GPU-accelerated queries). Drives data engineering excellence across the organization.

NoSQL Databases 1

▼

Writes basic SELECT queries in ClickHouse to retrieve data for BI reports. Understands column-oriented storage concepts and why ClickHouse is efficient for analytical workloads. Connects BI tools like Superset or Grafana to ClickHouse datasources and builds simple dashboards from pre-configured tables.

Designs materialized views in ClickHouse to pre-aggregate metrics for dashboard performance. Uses AggregatingMergeTree and SummingMergeTree engines to maintain real-time rollups. Writes complex queries with GROUP BY, HAVING, and nested subqueries to power interactive BI reports with sub-second response times.

Defines ClickHouse architecture standards for the BI platform, including cluster topology, replication policies, and data retention strategies. Establishes governance for materialized view lifecycle and schema evolution. Mentors analysts on query optimization patterns and coordinates ClickHouse upgrades with minimal dashboard downtime.

Drives organization-wide ClickHouse adoption strategy for BI analytics, evaluating trade-offs against alternatives like Druid or BigQuery. Designs multi-tenant ClickHouse architectures with resource isolation and cost attribution. Shapes the technical roadmap for real-time BI capabilities, integrating ClickHouse with streaming platforms and semantic layers.

Relational Databases 2

▼

Writes SELECT queries with JOINs and GROUP BY to pull reporting data from MySQL. Creates simple aggregation queries for dashboard metrics. Connects BI tools like Tableau or Power BI to MySQL data sources and builds basic visualizations from query results.

Builds optimized reporting views and materialized summary tables in MySQL for BI dashboards. Tunes aggregation queries using composite indexes and query execution plans. Configures live and extract connections in Tableau and Power BI with proper MySQL driver settings for reliable scheduled refreshes.

Establishes organization-wide standards for MySQL analytical layer design including naming conventions, view hierarchies, and aggregation table refresh strategies. Coordinates cross-team BI connectivity architecture ensuring consistent Tableau and Power BI data source configurations. Evaluates MySQL vs MariaDB feature trade-offs for enterprise BI workloads and drives technology selection decisions.

Defines enterprise data platform strategy positioning MySQL within the broader analytical ecosystem alongside data warehouses and lakehouse solutions. Architects multi-region MySQL deployments for global BI reporting with sub-second query latency requirements. Drives innovation in real-time BI by leveraging MySQL change data capture and streaming integration with modern BI platforms.

Understands basic PostgreSQL for BI work: writing SELECT queries with JOINs and aggregations, understanding table relationships for report building, basic query optimization with EXPLAIN. Follows team conventions for analytical query patterns and data warehouse schemas.

Independently designs analytical schemas and optimizes complex queries: writes performant multi-table JOINs with window functions, understands query execution plans for optimization, implements materialized views for report acceleration. Understands trade-offs between normalized schemas and analytical denormalization for BI workloads.

Defines PostgreSQL data strategy for BI teams: establishes standards for analytical schema design and query optimization, designs data warehouse architecture for enterprise reporting, drives adoption of PostgreSQL best practices for analytical workloads.

Defines organizational data strategy for enterprise analytics: evaluates PostgreSQL vs specialized analytical databases for organizational BI, designs multi-region data warehouse architectures, establishes governance for analytical data modeling and query optimization standards.

Web Frameworks 1

▼

Uses Python Web Frameworks at a basic level in Power BI/Tableau. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with Python Web Frameworks in Power BI/Tableau. Understands internals and optimizes performance. Writes tests using data validation.

Defines architecture for BI web applications and embedded analytics platforms using Flask and Dash. Establishes standards for report API design and caching strategies. Conducts design reviews of dashboard backends and defines the roadmap for self-service analytics tooling.

Defines company-wide strategy for BI web platforms, evaluating Dash, Streamlit, and Django for embedded analytics. Establishes enterprise standards for report API architectures, data access layers, and reference patterns for self-service analytics applications.

Additional skills (optional) 30

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

AI Coding Assistants

▼

Uses ChatGPT/Claude for writing SQL queries, DAX formulas, and data visualization descriptions. Applies AI assistants to interpret complex datasets and generate initial report narratives. Follows team guidelines for validating AI-generated analytical insights and dashboard configurations.

Independently applies ChatGPT/Claude for complex BI tasks — generating advanced DAX measures, optimizing Power BI data models, and creating automated report narratives. Understands trade-offs between AI-generated and manually crafted analytical logic. Designs effective prompts for data modeling and visualization tasks.

Defines ChatGPT/Claude adoption strategy for BI and analytics teams. Establishes standards for AI-assisted data analysis, automated reporting, and prompt libraries for analytical workflows. Conducts reviews of AI integration patterns in BI development ensuring data accuracy and responsible insight generation.

Uses Cursor IDE for writing and debugging analytical scripts in Python and SQL. Knows basic AI assistant features for generating SQL queries and data transformations. Applies Cursor for rapid prototyping of analytical solutions and data exploration.

Uses Cursor AI capabilities to accelerate ETL pipeline and analytical model development. Applies Cursor for automatic generation of dbt models and data quality tests from descriptions. Creates prompts for efficient work with analytical code: SQL optimization, data profiling.

Defines AI IDE usage strategy for the entire analytics team and standardizes workflows. Standardizes AI-assisted practices for analytics: templates, processes, productivity metrics. Fosters a culture of AI-augmented analytics development within the team.

Understands basic GitHub Copilot usage for BI work: leveraging suggestions for SQL query construction, using Copilot Chat for ETL logic questions, accepting completions for data transformation scripts. Follows team guidelines for validating AI-generated SQL and analytical code.

Independently configures GitHub Copilot for BI workflows: crafts effective prompts for complex SQL query generation, uses Copilot Chat for ETL optimization guidance, evaluates suggestion quality for data transformation accuracy. Understands trade-offs between AI-assisted query writing speed and data correctness validation.

Defines GitHub Copilot strategy for BI teams: establishes guidelines for AI-assisted SQL and ETL development, designs validation workflows for AI-generated data transformation code, drives adoption of Copilot best practices across analytics development processes.

Algorithms & Data Structures

▼

Understands basic sorting and search algorithms for optimizing SQL queries and data processing. Knows Big-O notation and can estimate performance of simple data operations in BI tools. Uses algorithm knowledge to choose efficient methods for dataset aggregation and filtering.

Applies algorithmic complexity knowledge to optimize ETL processes and analytical queries. Evaluates performance of different data processing approaches: window functions, recursive CTEs, graph traversals. Chooses optimal algorithms for data clustering and segmentation tasks.

Defines performance standards for analytical queries and ETL processes for the entire BI team. Reviews team's analytical solutions for algorithmic efficiency and scalability. Trains analysts in query optimization principles based on algorithm understanding.

Understands the fundamentals of Data Structures at a basic level. Applies simple concepts in work tasks using SQL/DAX. Follows recommendations from senior developers when solving problems.

Independently selects appropriate data structures for BI reports: star schema vs snowflake for data models, measure groups for calculation organization, hierarchy structures for drill-through navigation. Understands trade-offs between data model design choices for report performance.

Defines data structure standards for BI team: dimensional modeling conventions, data model review processes, metric definition governance. Conducts reviews of analytical data models for performance and clarity. Establishes team guidelines for report data structure optimization.

Application Security

▼

Understands basic data security principles and OWASP vulnerabilities relevant to BI systems. Knows fundamentals of personal data protection and can apply basic PII masking in reports. Recognizes responsibility when working with confidential data in analytical systems.

Applies data security principles when designing analytical reports and dashboards with PII. Implements row-level security and data masking for protecting confidential data in BI tools. Reviews SQL queries for injection vulnerabilities and ensures secure data access.

Defines data security strategy for the entire BI team: policies, tools, compliance requirements. Standardizes approaches to data protection in analytical systems and dashboards. Coordinates with the security team on audits and compliance checks for the BI platform.

Understands basic secure coding principles for protecting analytical scripts and data. Knows credential handling rules: no hardcoded passwords, use environment variables. Applies basic input validation when creating analytical scripts and ETL processes.

Applies secure coding practices when developing ETL pipelines: parameterized queries, secrets management. Implements secure handling of API keys and database credentials in analytical scripts. Conducts security review of analytical code for typical vulnerabilities: injection, exposure.

Defines secure development standards for the entire project's analytics team. Standardizes approaches to security in analytical code: tools, review process, compliance checks. Coordinates with the security team on security audits of analytical systems.

Caching

▼

Understands the caching concept and Redis's role in accelerating analytical queries and dashboards. Knows basic Redis operations and can check for cached report data. Uses caching to speed up recurring analytical queries in BI tools.

Configures Redis caching for analytical dashboards and APIs: TTL, cache keys, invalidation. Implements caching for heavy SQL query results and materialized views through Redis. Optimizes caching strategy for balancing data freshness and performance.

Defines caching strategy for the entire BI platform: policies, tools, monitoring. Standardizes approaches to analytical data caching for the development team. Coordinates cache architecture with dashboard and report performance requirements.

CI/CD

▼

Understands basic CI/CD concepts for automating deployment of analytical models and reports. Can run existing pipelines for updating dbt models or deploying dashboards. Knows GitHub Actions workflow structure and can read logs for error diagnosis.

Configures GitHub Actions for automatic testing and deployment of dbt models and ETL pipelines. Sets up CI pipelines for SQL query validation, data tests, and schema changes. Automates dashboard and report updates through scheduled workflows.

Defines CI/CD strategy for the entire analytics team: standards, workflow templates, best practices. Standardizes deployment automation for all BI platform components: models, dashboards, alerts. Coordinates with DevOps to create optimal infrastructure for analytics CI/CD.

Clean Code & Refactoring

▼

Understands the fundamentals of Code Quality & Refactoring at a basic level. Applies simple concepts in work tasks using SQL/DAX. Follows recommendations from senior developers when solving problems.

Independently applies code quality practices in BI development. Writes well-organized DAX measures and SQL queries with clear naming and documentation. Understands trade-offs between calculation performance and formula readability. Reviews report logic for metric consistency and data model integrity.

Defines code quality standards for BI team: DAX/SQL naming conventions, dashboard review processes, report template governance. Conducts architectural reviews of data model designs. Establishes quality gates for metric definitions and report publishing workflows.

Cloud Providers

▼

Understands basic AWS concepts for BI workloads — S3 for data lakes, Redshift for data warehousing, and QuickSight for dashboards. Uses ready-made configurations for data pipeline infrastructure. Performs simple operations like querying Athena tables and configuring QuickSight data sources under senior guidance.

Independently configures AWS services for BI pipelines — Glue ETL jobs, Redshift cluster management, and QuickSight dashboard deployments. Writes IaC for standard data warehouse infrastructure patterns. Understands VPC networking for secure data access and IAM policies for cross-account data sharing.

Defines AWS infrastructure strategy for enterprise BI platforms spanning data lakes, warehouses, and analytics services. Establishes IaC standards for Redshift/Glue/QuickSight deployments and data governance. Conducts architecture reviews optimizing data workload costs and drives FinOps for analytics compute spending.

Code Review

▼

Understands basic code review for BI assets: reviewing SQL/DAX query changes, checking dashboard calculation logic, validating report metric definitions. Follows team review guidelines for analytical code quality.

Independently conducts code reviews for BI assets: evaluates SQL/DAX query efficiency, reviews dashboard calculation logic, checks data model consistency across reports. Provides constructive feedback on analytical code readability and metric accuracy.

Defines code review strategy for BI team: SQL/DAX review standards, dashboard code review governance, analytical methodology review practices. Establishes review culture for analytical code quality and metric consistency.

Concurrency & Parallelism

▼

Understands the asynchronous concept for parallel data loading from multiple sources. Knows basic async/await patterns in Python for non-blocking work with APIs and databases. Can write a simple script for parallel data loading from REST APIs.

Implements asynchronous data loading from multiple API sources for ETL pipelines. Uses asyncio and aiohttp for parallel data extraction with rate limiting and error handling. Optimizes ETL execution time through concurrent processing of independent data streams.

Defines asynchronous data processing standards for the project's analytics team. Standardizes approaches to concurrent data processing and error handling in ETL systems. Coordinates real-time analytics architecture using async patterns.

Understands the concept of parallel data processing for accelerating analytical computations. Knows basic parallelism approaches in Python: threading, multiprocessing for data processing. Can use parallel pandas or Dask for accelerating simple transformations.

Applies multiprocessing for parallel processing of large datasets in ETL pipelines. Uses concurrent.futures for simultaneous data loading from multiple sources. Optimizes analytical scripts through proper work distribution between threads and processes.

Defines parallel data processing standards for the analytics team. Standardizes approaches to distributed processing and resource management in ETL systems. Coordinates selection of parallel computing tools for various analytical use cases.

Containerization

▼

Understands basic Docker concepts for BI work: containerized database instances for local development, Docker images for ETL tool dependencies, basic docker-compose for BI stack components. Uses ready-made configurations for analytics platform containers.

Independently configures Docker for BI environments: containerized database and ETL services, docker-compose for local analytics stack, Dockerfile for custom BI tool images. Understands container networking for multi-service BI architectures and volume management for data persistence.

Defines Docker infrastructure strategy for BI platforms: containerized analytics stack governance, data persistence and backup standards, container performance tuning frameworks. Conducts architecture reviews for containerized BI deployments and optimizes FinOps for analytics workloads.

Database Optimization

▼

Understands basic database indexing concepts: primary keys, foreign keys, and their impact on query performance. Recognizes when slow queries may benefit from index creation. Follows DBA recommendations on index usage for analytical queries.

Designs indexing strategies for analytical workloads: covering indexes for dashboard queries, partial indexes for filtered reports, and columnstore indexes for OLAP patterns. Understands query execution plans and can recommend index changes to DBAs. Balances index maintenance overhead with query performance gains.

Defines indexing strategy for the BI platform: standardized index management policies, performance benchmarks, and capacity planning for analytical workloads. Conducts reviews of database schema changes and their indexing impact. Establishes monitoring and alerting for index performance degradation across BI data stores.

Defines organizational data strategy for business intelligence: database technology selection for analytical workloads, multi-region data architecture, and real-time analytics infrastructure. Evaluates emerging technologies (vector databases, time-series databases) for BI use cases. Drives adoption of performance-centric data architecture across the organization.

Git & Workflows

▼

Understands basic Git advanced features for BI work: branching for report development iterations, cherry-pick for porting dashboard fixes between environments, stash for switching between analytics tasks. Follows team conventions for versioning SQL queries and report definitions.

Independently uses Git advanced features for BI projects: manages parallel report development branches with proper merge strategies, resolves conflicts in shared SQL and ETL scripts, implements hooks for query validation. Understands trade-offs between branching models for analytics team collaboration.

Defines Git strategy for BI teams: establishes branching standards for multi-environment report development, designs review workflows for analytical code changes, drives adoption of version control best practices for SQL and ETL pipelines across analytics teams.

Integration Testing

▼

Understands basics of integration testing for BI report data pipelines. Writes simple tests verifying data accuracy between source databases and dashboard datasets. Follows team testing guidelines for report refresh and data connection validation.

Writes integration tests for ETL pipelines: verifying end-to-end flow from source to warehouse. Tests integrations between data sources, transformations, and BI tools. Creates test fixtures and mock data for reproducible testing of analytical models.

Defines integration testing strategy for the entire project's analytics team. Standardizes approaches to testing data integrations and inter-system dependencies. Implements mandatory integration testing as a gate for deploying analytical changes.

Kubernetes & Orchestration

▼

Understands basic Kubernetes concepts for working with containerized analytical services. Knows main K8s objects and can check the status of analytical jobs in the cluster. Can read pod logs for diagnosing ETL process issues.

Deploys and manages analytical services in Kubernetes: Airflow, Spark, dbt runners. Configures CronJobs and resource limits for ETL processes and scheduled analytical tasks. Sets up persistent volumes for data and configures networking between analytical components.

Defines Kubernetes deployment standards for all analytical services of the team. Coordinates with the platform team on K8s infrastructure requirements for analytical workloads. Standardizes approaches to resource management and cost optimization for BI services.

Logging

▼

Understands basic structured logging for BI work: reading application logs for data pipeline debugging, understanding log-based metrics for ETL monitoring, basic log query syntax for investigating data processing issues. Follows team conventions for logging in analytical pipeline components.

Configures structured logging for BI pipelines: implements logging for ETL job tracking and data quality monitoring, creates dashboards for pipeline health visibility, sets up alerts for data processing anomalies. Analyzes pipeline incidents using log-based investigation.

Defines observability strategy for BI data platforms: establishes SLO-based approach for data pipeline reliability and freshness, coordinates data incident management across analytics teams, optimizes MTTD/MTTR for data quality incidents.

Metrics & Monitoring

▼

Understands basic Prometheus & Grafana for BI pipeline monitoring: reading ETL job performance dashboards, understanding data processing metrics for pipeline health, navigating Grafana panels for data freshness tracking. Follows team conventions for monitoring data pipeline infrastructure.

Configures Prometheus and Grafana for data pipeline monitoring: implements metrics for ETL job tracking and data freshness, creates dashboards for pipeline throughput and error rates, sets up alerts for data processing SLA violations. Analyzes data incidents using pipeline metric analysis.

Defines observability strategy for BI data platforms: establishes SLO-based approach for data pipeline reliability and freshness, coordinates data incident management, optimizes MTTD/MTTR for data quality incidents through improved monitoring.

Defines organizational observability strategy for data analytics with Prometheus & Grafana: implements enterprise data pipeline monitoring, builds data reliability culture across analytics teams, establishes enterprise SLO framework for data quality and freshness at scale.

Networking

▼

Understands basic network protocols for working with remote data sources and cloud services. Knows HTTP, TCP/IP basics for diagnosing database and API connection issues. Can use curl for testing data source availability.

Configures network connections to various data sources: VPN, SSH tunnels, SSL/TLS. Diagnoses network issues when working with cloud data warehouses and remote databases. Optimizes network settings for accelerating large data transfers between systems.

Defines network configuration standards for the entire team's analytical systems. Coordinates with network engineers on connectivity requirements for the analytical platform. Standardizes approaches to secure networking for data pipelines and BI tools.

OOP & Design Patterns

▼

Knows basic design patterns for structuring analytical code: Factory, Strategy. Understands the purpose of patterns for creating modular ETL components and analytical tools. Applies simple patterns for organizing data loading and transformation code.

Applies design patterns for creating flexible ETL pipelines: Pipeline, Builder, Adapter. Uses Strategy for switching between data sources and aggregation methods. Implements Observer for data quality monitoring and automatic anomaly alerting.

Defines architectural pattern standards for the entire team's analytical systems. Reviews analysts' architectural decisions and recommends optimal patterns for each task. Creates an internal pattern library for typical BI tasks: ETL, reporting, data quality.

Understands basic OOP principles for writing structured Python code in analytical scripts. Knows class and module concepts for organizing reusable analytical code. Creates simple classes for encapsulating data processing and report generation logic.

Applies OOP principles for creating modular ETL components and analytical libraries. Designs transformer classes with clear separation of concerns for the data pipeline. Uses inheritance and polymorphism for creating flexible connectors to various data sources.

Defines OOP design architectural standards for all analytical scripts and libraries of the team. Reviews analysts' code for compliance with clean code and modular design principles. Trains BI analysts in applying OOP for writing maintainable analytical code.

Prompt Engineering

▼

Uses AI assistants to generate SQL queries and data transformation scripts. Formulates clear prompts describing desired data output format and business context. Iterates on prompts to refine analytical results.

Creates reusable prompt templates for recurring analytical tasks (cohort analysis, funnel breakdowns, trend detection). Chains prompts to build complex analytical workflows — from data exploration to insight generation. Optimizes prompts for token efficiency while maintaining output quality.

Defines prompt engineering strategy for the BI department. Establishes standards for AI-assisted analytics: prompt templates, output validation, and quality metrics. Creates governance framework for AI usage in business reporting — accuracy requirements, human review gates, and audit trails.

Defines enterprise AI-augmented analytics strategy. Establishes organizational standards for AI-assisted decision-making in business intelligence — accuracy benchmarks, governance policies, and ethical guidelines. Drives cross-departmental AI adoption for data-driven insights. Shapes vendor relationships and model procurement strategies for BI use cases.

REST API

▼

Understands basic REST API concepts for BI workflows: fetching data from APIs for reports, API query parameters for data filtering, basic authentication for data source APIs. Follows team conventions for API-based data extraction.

Independently works with REST APIs for BI: designing data extraction API calls, implementing pagination for large dataset retrieval, building API-based data connectors for BI tools. Understands best practices for API data caching and efficient query parameter usage.

Defines API strategy for BI data access at product level: data API layer standards for reporting, API-based data integration governance, analytics API performance requirements. Conducts API architecture reviews for BI platform integrations.

Search Engines

▼

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

Independently implements tasks with Elasticsearch / OpenSearch in Power BI/Tableau. Understands internals and optimizes performance. Writes tests using data validation.

Defines Elasticsearch/OpenSearch architecture for BI organization: index lifecycle management, Kibana space governance, and dashboard performance standards. Establishes search query optimization guidelines and data retention policies.

Defines enterprise Elasticsearch/OpenSearch strategy for BI and observability. Evaluates search platform evolution and migration paths. Shapes organizational standards for search-powered analytics and real-time data exploration.

System Design

▼

Understands basic system design concepts for contextual analysis of data architecture. Knows main data infrastructure components: source systems, ETL, warehouse, BI tools. Can describe a simple analytical system architecture: sources, storage, dashboards.

Designs architecture of analytical subsystems: data warehouse schema, ETL pipeline, caching layer. Evaluates trade-offs when choosing technologies: batch vs streaming, star vs snowflake schema. Develops system design for medium-scale analytics platforms.

Defines the analytical platform's system architecture and coordinates technical vision. Conducts design reviews and makes architectural decisions for the entire project's data platform. Mentors senior analysts in system design skills for analytical systems.

Unit Testing

▼

Understands basic testing for BI assets: data validation checks in reports, simple calculated field verification, dashboard data accuracy tests. Follows team practices for verifying metric calculations and report output correctness.

Writes tests for ETL pipelines and analytical transformations: type checking, null values, business rules. Creates data quality tests for monitoring data integrity in the warehouse. Uses dbt tests and Great Expectations for automatic data validation.

Defines data testing and quality assurance standards for the project's BI team. Standardizes approaches to data testing: tools, coverage requirements, CI/CD integration. Fosters a data-quality-first culture through mandatory testing of all analytical models.