What a Junior } should know

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.