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How to become a Data Engineer

Build the pipelines everything else depends on: ingestion, transformation, warehousing and orchestration.

21-29 weeks4 phases15 steps25 free resources3 checkpoint tests

Best for

Students who like backend engineering and data, and want a role with strong demand and a friendlier applicant-to-opening ratio than ML.

Maybe not for you if

If you want to answer business questions rather than build the plumbing, Data Analyst is the better fit.

The 4 phases

What each phase gets you to. The steps, resources and checkpoint inside each one open with a free account.

  1. Phase 1: Python and SQL, the two non-negotiables

    5-7 weeks

    Goal: Write clean, tested Python and multi-step analytical SQL comfortably, and know why a query is slow.

    4 steps · ends in a scored checkpoint (65% to clear) · assumes basic programming

  2. Phase 2: Data modelling and systems basics

    4-6 weeks

    Goal: Design an analytics schema you can defend choice by choice, and know what your code is doing to the machine underneath.

    3 steps · ends in a build deliverable

  3. Phase 3: Pipelines, orchestration and cloud

    7-9 weeks

    Goal: Run a scheduled, tested pipeline on real infrastructure that survives failure and can be re-run and backfilled safely.

    5 steps · ends in a scored checkpoint (60% to clear)

  4. Phase 4: Clear the process

    5-7 weeks

    Goal: Pass the coding, SQL and written rounds that gate data roles, and turn the flagship into interview answers.

    3 steps · ends in a scored checkpoint (60% to clear)

Open the full Data Engineer roadmap

Free account, no card. It takes about a minute and you do not need to verify your email to start.

  • All 15 steps, in order, with why each one is there
  • 25 hand-picked free resources, no paid course upsells
  • The specific mistake people make at each stage
  • 3 scored checkpoint tests, so progress is earned not ticked
  • Progress saved per step, so a break does not cost you the thread
  • Adaptive start, phases your test history already clears are skipped
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What hiring actually looks like here

Practitioners commonly describe the working order as Python, then SQL, then orchestration, then cloud, then portfolio: typically around a year of consistent effort. Indian screens in 2026 usually pair a standard coding round with a SQL round, and hiring teams weigh one deep pipeline you can explain and defend over a wall of tutorial repos.

We claim no placement outcome, salary or success rate. This describes what is commonly reported about the role, nothing more. Linked resources are credited to their authors.

Comparing paths?