Data Engineer — Professional

What you'll learn

Course modules

1. 01

   Data engineering foundations — what DEs actually do and the modern stack

   Data engineering is the discipline of moving and shaping data reliably at scale. This module sets the context: what DEs actually do day-to-day, how DE differs from data analysis and ML engineering, the modern data stack, and the career path for international data engineers.

   ~450min
2. 02

   SQL deep-dive — window functions, CTEs, query optimisation

   SQL is the most valuable skill for any data engineer. This module goes beyond basics: window functions, CTEs, query optimisation, execution plans, and the analytical SQL patterns used daily in production warehouses.

   ~450min
3. 03

   Data modeling — dimensional, normalised, data vault

   How you model data determines whether your warehouse is fast, useful, and maintainable — or a swamp. This module covers the three main approaches (Kimball dimensional, 3NF normalised, data vault), when each applies, and slowly-changing dimension patterns.

   ~450min
4. 04

   ETL vs ELT, batch vs streaming, ingestion patterns

   Data engineering is fundamentally about moving and shaping data. This module covers the patterns: ETL vs ELT, batch vs streaming, change data capture, idempotency, and the tooling landscape (Fivetran, Airbyte, Kafka, custom Python).

   ~450min
5. 05

   Apache Airflow — orchestration in production

   Airflow is the dominant workflow orchestrator in 2026. This module covers DAGs, operators, schedule semantics, dependency management, and the production patterns (retries, alerting, resource pools, deployment) that separate hobby Airflow from enterprise Airflow.

   ~450min
6. 06

   dbt + cloud data warehouses — the modern transformation layer

   dbt revolutionised analytics engineering. This module covers dbt fundamentals (models, sources, tests, macros, packages), the major cloud warehouses (Snowflake, BigQuery, Redshift), and the patterns that scale dbt projects from 5 to 5000 models.

   ~450min
7. 07

   Apache Spark — distributed processing for large-scale data

   When data exceeds warehouse capabilities or you need complex programmatic transforms, Spark handles it. This module covers Spark fundamentals, DataFrames, partitioning, performance tuning, and when Spark is appropriate (and when it is not).

   ~450min
8. 08

   Data quality, observability, governance, and career paths

   Building pipelines is half the job. Keeping them healthy, trustworthy, and compliant is the other half. This final module covers data quality testing, observability, governance, and the career trajectories from junior to principal data engineer.

   ~450min