What a Junior } should know

Executes analytical SELECT queries in ClickHouse with filtering, sorting, and basic aggregations. Understands the difference between ClickHouse and row-oriented databases for analytical workloads. Runs simple cohort queries using date functions and GROUP BY to analyze user behavior patterns over time.

Understands relational data modeling fundamentals: normalization, primary/foreign keys, and entity relationships. Follows team patterns for structuring analytical tables. Uses ER diagrams to understand data dependencies for accurate analysis.

Understands how indexes affect query execution time on analytical datasets. Knows the difference between clustered and non-clustered indexes. Can check if existing indexes cover frequently used WHERE and JOIN columns in analytical queries.

Extracts datasets from MySQL using filtered SELECT queries with WHERE, JOIN, and basic aggregation. Creates temporary tables to stage intermediate analysis results. Writes exploratory SQL queries to validate data quality and identify patterns in analytical workflows.

Understands basic PostgreSQL for data analysis: writing analytical queries with window functions, understanding table structures for exploratory analysis, basic CTEs for complex data extraction. Follows team conventions for analytical query organization and data access patterns.

Understands query optimization basics for data analysis: execution plan interpretation, common slow query patterns, and the impact of data volume on query performance. Follows team practices for writing performant analytical queries and using query profiling tools.

Understands basic Airflow concepts and DAG scheduling. Monitors data pipeline runs that feed analytical datasets. Follows team documentation to trigger ad-hoc DAG runs for data refresh and extraction tasks.

Creates exploratory dashboards with basic filters and drill-downs. Understands standard chart types for statistical visualization. Builds simple cohort views and follows team conventions for dashboard layout and naming.

Understands data catalog basics and metadata search capabilities. Uses catalog to find relevant datasets for analytical queries. Documents dataset descriptions and column-level metadata following team standards.

Understands data contract fundamentals and their role in ensuring data reliability. Follows contract schemas when writing analytical queries. Identifies and escalates data quality issues that violate established contracts.

Understands data lineage fundamentals and their importance for data trust. Uses lineage visualization to understand how analytical tables are constructed from raw sources. Documents query dependencies for reproducible analysis.

Applies data quality checks using pandas and SQL before analysis. Validates datasets for completeness in Jupyter notebooks. Uses profiling tools to detect missing values and outliers. Follows team conventions for data cleansing in Superset reports.

Queries warehouse tables using correct join patterns between facts and dimensions. Understands the purpose of analytical schemas and navigates star schema structures to extract meaningful datasets. Writes efficient SELECT statements leveraging partitioning and pre-aggregated tables for routine analysis tasks.

Understands dbt fundamentals: models, tests, and documentation. Follows team patterns for building analytical transformations from staging to mart layers. Uses dbt-generated documentation to discover available datasets for analysis.

Understands Pandas basics for analytical workflows: data loading from multiple sources, exploratory data analysis with describe/info/value_counts, and basic data visualization with matplotlib integration. Cleans datasets by handling missing values and type conversions. Follows team coding standards for Jupyter notebooks.

Understands SQL-based ETL fundamentals for analytical datasets. Writes basic data extraction and cleaning queries. Follows established pipeline patterns to prepare filtered datasets for ad-hoc analysis requests.