AI Coding Assistants 1
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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.
Designs GitHub Copilot adoption strategies for BI teams: optimizes prompt engineering for complex SQL and ETL code generation, implements validation for AI-generated data transformation logic, measures impact on analytics development velocity. Mentors analysts on effective AI-assisted data engineering practices.
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 1
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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.
Selects optimal data structures for BI workloads: star/snowflake schemas for analytical query patterns, bridge tables for many-to-many relationships, slowly changing dimension types for historical tracking. Optimizes Power BI data model structures for DAX calculation performance. Designs hierarchical dimension structures for drill-down analytics.
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.
Batch Processing 3
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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.
Designs dbt project architecture for enterprise BI platform with semantic layer integration. Implements dbt Mesh patterns for cross-project model references. Architects multi-environment deployment strategies with slim CI and state-based builds.
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.
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.
Designs data processing architecture with Pandas/Polars for enterprise BI: automated ETL pipelines, data quality frameworks, and real-time analytics data preparation. Optimizes large-scale data transformations with Polars lazy evaluation and partitioned processing. Implements data governance practices including lineage tracking and schema validation. Mentors team on efficient data engineering patterns.
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.
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.
Architects end-to-end ETL for enterprise BI warehouses. Designs incremental load strategies, optimizes star/snowflake schema refresh cycles, and implements data quality gates ensuring report-ready datasets across business domains.
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.
Clean Code & Refactoring 1
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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.
Designs code quality standards for BI codebases: SQL/DAX naming conventions, dashboard-as-code practices, report template standardization. Refactors complex Power BI/Tableau calculated fields for maintainability. Establishes review processes for data model clarity and metric consistency across reports.
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.
Data Governance 3
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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.
Designs data catalog architecture for enterprise BI ecosystem. Implements automated metadata harvesting from BI tools (Tableau, Power BI, Looker). Establishes metric governance with certified dataset programs and ownership models.
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.
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.
Designs data contract architecture for enterprise BI platform. Implements contract versioning, backward compatibility checks, and automated SLA monitoring. Establishes contract-first approach for new data source onboarding.
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.
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.
Designs data lineage architecture for enterprise BI ecosystem. Implements end-to-end lineage from source systems through ETL to dashboards. Establishes automated impact analysis workflows for schema evolution and migration planning.
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.
Data Modeling 1
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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.
Designs enterprise-grade dimensional models spanning multiple business domains. Implements Data Vault 2.0 patterns for historical tracking and auditability. Architects semantic layers that bridge physical data models with business-friendly abstractions.
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.
Data Orchestration 1
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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.
Designs Airflow-based data pipeline architecture for enterprise BI platform. Implements complex dependency graphs across multiple data sources with SLA monitoring. Mentors team on DAG design patterns for reporting workflows.
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.
Data Quality 1
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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.
Architects data quality frameworks across Tableau, Power BI, and Superset. Designs end-to-end validation strategies for BigQuery and ClickHouse warehouses. Implements automated lineage tracking and quality scoring for business KPIs. Mentors team on data governance and quality-first culture.
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.
Data Visualization 1
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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.
Designs enterprise BI architecture across Tableau, Power BI, and Looker with governed data models and semantic layers. Optimizes dashboard ecosystems for thousands of concurrent users. Implements data quality frameworks and row-level security for executive reporting.
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.
Data Warehousing 1
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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.
Architects the BI semantic layer across the entire warehouse, defining conformed dimensions and standardized metrics. Drives schema design decisions that balance reporting flexibility with query performance at scale. Mentors junior analysts on proper schema usage and establishes governance practices for aggregate table lifecycle management.
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.
Database Optimization 2
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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.
Designs indexing architecture for enterprise BI systems: materialized view indexes for pre-computed aggregations, bitmap indexes for low-cardinality dimensions, and index strategies for real-time dashboards. Implements index lifecycle management (creation, monitoring, deprecation). Optimizes cross-database query performance for data warehouse and operational stores.
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.
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.
Designs query architecture for enterprise BI: materialized view refresh strategies, query pushdown optimization for federated data sources, and adaptive query routing based on data volume. Implements query performance monitoring with automated regression detection. Creates query optimization guidelines and review processes for BI teams. Mentors analysts on advanced SQL optimization.
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.
Logging 1
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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.
Designs observability strategy for BI data infrastructure: implements end-to-end tracing for data pipelines, defines SLI/SLO for data freshness and quality metrics, conducts post-mortems for data incidents. Mentors analysts on log-based debugging for complex data pipeline issues.
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.
NoSQL Databases 1
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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.
Architects ClickHouse schemas optimized for BI workloads, selecting appropriate MergeTree family engines and partitioning strategies. Builds cascading materialized view pipelines for multi-level aggregation. Tunes query performance through projection usage, skip indices, and dictionary-based dimension lookups for enterprise-scale dashboards.
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.
Relational Databases 2
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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.
Designs MySQL analytical layer architecture with pre-aggregated tables, partitioned fact tables, and star-schema views optimized for BI tool consumption. Implements row-level security patterns in MySQL that integrate with Tableau and Power BI access controls. Mentors team on writing performant analytical queries and proper use of query hints for reporting workloads.
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.
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.
Designs PostgreSQL architecture for enterprise BI workloads: optimizes data warehouse schemas for complex analytical queries, implements columnar storage extensions for OLAP performance, configures read replicas for report isolation. Mentors analysts on advanced SQL optimization and data modeling for analytics.
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.
Web Frameworks 1
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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.
Builds internal analytics dashboards and reporting APIs using Flask and Dash. Optimizes query endpoints for large dataset aggregation. Chooses between lightweight and full-stack frameworks for BI tooling. Mentors analysts on building self-service data apps.
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.