A Data Engineer designs and runs the plumbing behind every dashboard, ML model, and product metric: pipelines, warehouses, streaming systems. Companies of every size need you, from Series A startups to banks. This template helps you show recruiters the scale of data you handled and the dollars you saved.
Copy these as starting points and swap in your own numbers.
2024–2025 estimates. Wide ranges by experience and seniority.
Yes, those are the two most common entry paths. Analysts usually pick up SQL and modeling faster, while backend engineers nail the engineering side: orchestration, testing, CI/CD. Close the gap on whichever is weaker and ship a real pipeline as a side project.
No. Solid expertise in one cloud is enough. Once you know AWS well, you'll pick up GCP in a week, the concepts are the same, only the service names differ.
Data volumes and architecture details usually don't break NDAs. Write '12 TB DWH', '40+ Airflow DAGs', '600M events per month'. Replace client names and specific business metrics with 'fintech client' or 'retail project'.
Very. It's effectively the standard for transformations in the modern data stack. If a Mid or Senior role doesn't use dbt, either it's a legacy stack or they expect you to introduce it. Either way, it's worth learning well.
One serious project is enough: real data source, Airflow, dbt, tests, and a dashboard in Metabase or Superset. That proves you've seen the full lifecycle, not just YouTube tutorials.