- Career Roadmaps
- Data Engineer

Data Engineer Roadmap

You build the pipelines that move, clean and serve data - the plumbing every analytics and AI team depends on. Indicative 2026 entry salaries
$75-100k US
, often reached via a first analyst or backend seat. SQL + Python + one warehouse is the hiring triangle.

By Carl Mills
- Last updated 10 July 2026
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Stage 1 - Foundations 4 item(s)
- Data modelling: normalisation for apps vs star schemas for analytics Critical
- Git + basic Linux/shell - pipelines live on servers Critical
- Python for data work: functions, error handling, requests, pandas basics Critical
- SQL deep: window functions, CTEs, query plans, indexes Critical

Stage 2 - Pipelines 4 item(s)
- Build an ETL: pull an API, clean it, load Postgres - scheduled daily Critical Your first portfolio piece. Cron is fine; the discipline is the point.
- dbt: models, tests,



documentation - analytics engineering in practice Critical
- Idempotent + restartable pipelines: reruns must not duplicate data Critical
- One orchestrator (Airflow or Dagster): DAGs, retries, backfills

Stage 3 - Platforms 5 item(s)
- Data quality: tests, freshness alerts, contracts with producers Critical
- One cloud warehouse hands-on: loading, partitioning, cost awareness Critical
- AI-assisted pipeline development - generate, then verify against real data
- File formats & lakes: Parquet vs CSV, object storage, when lakes beat warehouses
- Streaming literacy: what Kafka solves, batch vs stream trade-offs

Stage 4 - Job-Ready Proof 4 item(s)
- CV/LinkedIn target "Data Engineer" naming SQL, Python, dbt, your warehouse Critical
- End-to-end public project: source -> pipeline -> warehouse -> dashboard Critical
- Passed a mock interview including pipeline design questions Critical
- Architecture write-up: decisions, failure handling, costs

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