Data Engineer (AI/ML)
You build and run the pipelines that get data to the models, reliably and on time.
Degree usually expected Moderate-to-hard. Lower barrier than model research, though we have not benchmarked that.
Can I actually do this?
A realistic way into AI/ML work for someone already in tech, because the skills transfer from ordinary backend and analytics work. We have not verified hiring volumes against a public labour-market source, so we are not going to claim this family's jobs are mostly here.
Who it suits. People who like plumbing more than modelling, and who take quiet satisfaction in a job that runs at 3am without waking anyone.
Runway. Realistic from a software, analytics or cloud background; not from a standing start.
Coming from backend, analytics or cloud work? This is the shortest real bridge into the AI/ML family. Cloud Engineer
Also advertised as
The route
Four stations, in order. Each one is a thing you finish before the next matters.
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Station one
Learn it free
Only the best few, deliberately. Every one of these is free to use — the pill on each card says exactly what is and isn't free.
Microsoft Learn — Data Engineer career path
Free to learn · no certificate
Free structured career path from Microsoft covering ingestion, storage and transformation.
Verified 2026-07-26
Apache Airflow official tutorial
Free to learn · no certificate
Orchestration is a core daily skill and Airflow's own tutorial is free. We have not measured how often Airflow specifically is named in job ads.
Verified 2026-07-26
dbt documentation — introduction
Free to learn · no certificate
Transformation-as-code. The documentation is free to read; dbt Core is open source, though we have not verified the boundary between free and paid tiers on their pricing page.
Verified 2026-07-26
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Station two
Attest strategically
Optional, and buy it late. Pipelines you can show beat a certificate here more than in any other role on this site. If you do take it, check the 2-year validity — it expires faster than Google's ML Engineer certificate.
Google Cloud Professional Data Engineer
Google Cloud · Professional Data Engineer
Free to learn · paid certificate Recognized
The honest cost Cost $200
Plus tax where applicable. Printed on Google's own exam page.Validity 2 years — note this is SHORTER than Google's ML Engineer certificate, which runs 3 years. Renewal Renew within the eligibility period before expiry via Google's renewal process. Assessment Proctored exam — two hours, 40–50 multiple choice and multiple select questions, online- or onsite-proctored Proctored Yes Verify via unknown Cost per active year $100/yr200 ÷ 2 Google's role-mapped data engineering credential. Recommended experience is 3+ years in industry including 1+ year on Google Cloud.
Read this before you buyWatch the validity difference: this certificate lasts 2 years where Google's ML Engineer lasts 3, so it costs $100 per active year against the ML Engineer's $67. Same sticker price, meaningfully different running cost.
Verified 2026-07-26 · Official page
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Station three
Prove it
A certificate says you passed a test. These say you can do the job.
An orchestrated pipeline that recovers
Build a scheduled pipeline that ingests, transforms and lands data, then deliberately fail a task and show it retrying and recovering. Recovery behaviour is what interviewers probe.
Tested transformations
Model a real dataset with dbt including tests and documentation. Data quality tests are the difference between a script and an engineering artifact.
A cost and freshness write-up
Document what your pipeline costs to run and how stale the data can get before anyone notices. Both are questions you will be asked on the job.
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Station four
Get hired
Search these exact titles
Who hires for this. Any company with more data than it can query by hand — product companies, retail, finance, healthcare, and every AI team that needs training data.
Pipelines you can show carry this role. If you want to work near AI using skills you may already have, this is a shorter bridge than the research-facing roles — a judgement from the skill overlap, not from a hiring-volume source we have verified.
On salaryWe don't publish salary estimates. Numbers copied between blogs drift from reality, and a wrong number costs you real negotiating power. When we have a verified public source, it goes here with its date.
Where this route continues
- Machine Learning Engineer — the common next step
- LLM Engineer — if you move toward applications
- Cloud Engineer — sideways move
This page last verified 2026-07-26 · How we verify
All three resources fetched live 2026-07-26 (HTTP 200). Credential facts quoted verbatim from Google's official exam page. REPLACES the knowledge graph's single official_resource for this path, https://aws.amazon.com/what-is/data-engineering/, which is the one confirmed 404 in the 905-URL census.