Analytics Engineer

☐ SQL (Advanced) · Required · 10 hrs

Window functions, CTEs, subqueries, EXPLAIN / query plans, optimisation. Analytics engineers live in SQL — go deep, not shallow.

• Your First Query — SELECT & FROM (SQL): https://chaduvuko.com/learn/sql/select-from
• Introduction to Joins (SQL): https://chaduvuko.com/learn/sql/joins-intro
• Grouping Data — GROUP BY (SQL): https://chaduvuko.com/learn/sql/group-by
• Subqueries — Queries Inside Queries (SQL): https://chaduvuko.com/learn/sql/subqueries

☐ Python Basics · Recommended · 5 hrs

Enough to write Airflow DAGs, use the dbt Python SDK, and parse data files. You don't need to be a software engineer.

• What is Python? Setup & Your First Program (Python): https://chaduvuko.com/learn/python/what-is-python-setup
• Functions — Defining, Parameters, Return Values (Python): https://chaduvuko.com/learn/python/functions
• Reading & Writing Files (Python): https://chaduvuko.com/learn/python/reading-writing-files
• Exception Handling (Python): https://chaduvuko.com/learn/python/exception-handling

☐ Git & Version Control · Required · 2 hrs

dbt projects live in Git. Branches, PRs, code review — treat your SQL models as software.

• Git and Version Control for Data Projects (Data Engineering): https://chaduvuko.com/learn/data-engineering/git-for-data

☐ Data Modelling · Required · 7 hrs

Kimball dimensional modelling, facts and dimensions, star schema, slowly changing dimensions. The mental model for analytics data design.

• Data Modelling — Dimensional, Star and Snowflake Schema (Data Engineering): https://chaduvuko.com/learn/data-engineering/data-modelling
• Slowly Changing Dimensions — SCD Types 1, 2 and 3 (Data Engineering): https://chaduvuko.com/learn/data-engineering/slowly-changing-dimensions

☐ Data Warehouse (BigQuery / Snowflake / Redshift) · Required · 6 hrs

Columnar storage, partitioning, clustering, cost optimisation, warehousing concepts. Pick one and go deep — all are widely used.

• Data Warehouse Concepts — Columnar Storage and Distribution (Data Engineering): https://chaduvuko.com/learn/data-engineering/warehouse-concepts
• Google BigQuery (Google Cloud): https://chaduvuko.com/learn/gcp/bigquery
• What is Snowflake? (Snowflake): https://chaduvuko.com/learn/snowflake/what-is-snowflake
• Amazon Redshift (AWS): https://chaduvuko.com/learn/aws/redshift

☐ dbt (Core) · Required · 10 hrs

Models, sources, tests, macros, snapshots, documentation, lineage graph. dbt is the tool that defines the analytics engineer role.

• What is dbt? (dbt): https://chaduvuko.com/learn/dbt/what-is-dbt
• Models: SELECT Statements as the Building Block (dbt): https://chaduvuko.com/learn/dbt/models-basics
• Incremental Models in Depth (dbt): https://chaduvuko.com/learn/dbt/incremental-models
• Testing: Generic and Singular Tests (dbt): https://chaduvuko.com/learn/dbt/testing-basics

☐ Data Quality & Testing · Required · 5 hrs

dbt tests (unique, not_null, accepted_values), Great Expectations, data contracts, anomaly detection. Untested data is misinformation.

• Testing: Generic and Singular Tests (dbt): https://chaduvuko.com/learn/dbt/testing-basics
• Testing Strategy and Data Quality at Scale (dbt): https://chaduvuko.com/learn/dbt/testing-strategy-at-scale

☐ Orchestration (Airflow / Dagster) · Required · 6 hrs

DAG design, scheduling, sensors, task dependencies. Data pipelines need reliable scheduling.

• Pipeline Orchestration — What a Scheduler Does (Data Engineering): https://chaduvuko.com/learn/data-engineering/pipeline-orchestration
• Cloud Composer (Google Cloud): https://chaduvuko.com/learn/gcp/composer

☐ Documentation & Lineage · Required · 3 hrs

dbt docs, column-level lineage, data catalogue (DataHub / Atlan), metric definitions. Good documentation is the analytics engineer's superpower.

• Documentation: Descriptions, Doc Blocks, and dbt Docs (dbt): https://chaduvuko.com/learn/dbt/documentation
• Data Governance — Catalogues, Lineage and Access Control (Data Engineering): https://chaduvuko.com/learn/data-engineering/data-governance

☐ BI Tools (Metabase / Tableau) · Required · 5 hrs

Build dashboards, calculated fields, performance optimisation. Analytics engineers serve data consumers — know their tools.

☐ Metrics Layer (dbt Semantic) · Optional · 5 hrs

dbt Semantic Layer, MetricFlow, centralised metric definitions. The future of consistent business metrics across tools.

☐ Reverse ETL · Optional · 3 hrs

Census, Hightouch — sync warehouse data back to CRMs, marketing tools, support platforms. Operational analytics is growing fast.