Questions and Answers

DE-specific questions. For general zoomcamp logistics (joining, deadlines, certificate, project flow), see Zoomcamp Logistics. For module-specific and technical issues, check the Data Engineering Zoomcamp FAQ.

Why Google Cloud Platform instead of AWS or Azure?

Three reasons:

  • $300 free credits for new accounts. AWS’s free tier is limited and many services exclude the free tier; Azure’s $200 expires after 30 days. GCP gives the most generous credits with the fewest service restrictions.
  • No service limitations during the free trial. The course exercises all run on the free tier.
  • Historical compatibility. dbt worked better with BigQuery when the course launched, and the curriculum was built around the GCP stack.

For the project, you can use any cloud. See Environment Setup for notes on alternatives.

Can I get a DE job without a degree?

Short answer: yes.

Multiple success stories from past cohorts:

  • Students who landed jobs through the course network.
  • Bruno (no formal degree, now Senior DE at US companies).
  • Career switchers from analytics, support, and unrelated fields.

What works:

  • A strong portfolio of projects.
  • Networking (learning in public helps).
  • Consistency and going beyond the basics.

Is 31 (or 35, or 40) too old to start?

No. In data engineering, the industry cares about skills, not age. In Germany, many DEs start their careers at 30+ after extended education. Career switchers from other engineering, finance, or analytics backgrounds are common.

Tip: do not put your age on your CV to avoid unconscious bias.

How much time does the course take?

  • Two weeks for Module 1, one week each for modules 2 to 6.
  • Two to three weeks for the project.
  • Plan for 10 to 15 hours per week.

See Prerequisites for details.

What is NOT covered (the other 80%)?

The course covers the essential 20% that handles 80% of real-world DE work. Areas the course does not cover that you will encounter in real DE jobs:

  • Polars: a modern pandas alternative for data manipulation.
  • Delta Lake / Apache Iceberg: advanced table formats for data lakes.
  • Snowflake / Redshift / ClickHouse: other data warehouses (the course uses BigQuery).
  • dbt incremental models: optimizing dbt for large datasets.
  • Apache Flink: real-time streaming. Module 6 introduces it but only briefly.
  • Data governance and catalogs: DataHub, Unity Catalog.
  • Databricks: increasingly common in industry but not in the course.

The remaining 80% comes with experience in your first DE role.

Final thoughts

Data engineering is a field that’s “5 feet deep and 50 miles wide.”

  • Ask questions in Slack (after checking the FAQ and the Q+A in this section).
  • Learn in public.
  • Help your peers.
  • Do not get discouraged. It gets challenging.
  • You got this.