What a Junior } should know

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.

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.

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.

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.

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.

Writes readable SQL queries, avoiding SELECT * and unnecessary JOINs. Uses EXPLAIN to understand basic execution plans. Follows team recommendations for writing efficient dbt models.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.