Data Platform & Analytics
Data Platform Engineer
Builds the substrate the data engineers use. The paved road for data, with the same adoption problem every platform has.
Where a data engineer builds pipelines, a data platform engineer builds the thing pipelines are built on — ingestion framework, orchestration, the warehouse itself, the catalogue, the access model and the cost controls. Your customers are internal and they can build their own if yours is worse.
| Time | What you are actually doing |
|---|---|
| 09:00 | Warehouse cost review. One team's pipeline is 40% of the bill and they do not know, because nothing attributes spend. |
| 10:30 | Ingestion framework work — making a new source a config file rather than a bespoke pipeline. |
| 13:00 | Access model. Deriving grants from classification instead of from tickets, so nobody has to ask. |
| 14:30 | A team has built their own scheduler because ours was awkward. The interesting question is which part was awkward. |
| 16:00 | Upgrading the orchestrator across every pipeline, with a codemod rather than forty tickets. |
The decisive round
“Design a data platform for 200 engineers and 50 analysts.”
Interviewer: “Greenfield. What do you build first?”
A weak answer. “I'd set up a lakehouse with a medallion architecture, an orchestrator, a transformation layer, a catalogue and a BI tool, with CI/CD for the pipelines.” A reference architecture recited, in an order determined by a vendor diagram rather than by what unblocks anyone.
A strong answer. “I would not build the architecture first. I would find the two or three things people wait for, because that is what determines whether anyone uses it.
Usually it is: getting a new source in, getting access to data they can see is there, and knowing whether a number is trustworthy. So the first thing I build is ingestion as configuration — adding a source is a file, not a bespoke pipeline — because that removes the largest queue and buys the credibility to do everything else.
Then access derived from classification rather than from tickets, so the answer to ‘can I see this’ is automatic and the security team gets a model instead of a backlog.
Then the layered model — raw append-only, staging, core, marts — because without it we get six definitions of customer and the platform becomes a place numbers are argued about.
Then cost attribution from day one. Not optimisation — attribution. Tag warehouses and queries by team, because without it nobody owns anything and every cost conversation is about the total.
What I would deliberately defer: the catalogue, which is worth little until there is something worth cataloguing, and the semantic layer, which people build early and then find nobody adopts. And I would measure adoption — percentage of sources on the framework, time from request to data available — because a platform nobody uses is indistinguishable from one that does not exist.”
Building for the queue rather than the diagram, deferring the catalogue, and treating cost attribution as foundational rather than an optimisation phase.
- Build ingestion-as-config for two different source types and show the second one took an hour.
- Implement classification-derived access on something real, then test it with two users of different privilege.
- Produce a cost-per-team report from billing data. The unattributed percentage is always the finding.
- Have an adoption number and its trend, because this is a platform role and adoption is your revenue equivalent.
Compensation
| Market | Band | Notes |
|---|---|---|
| United States | $150k – $250k base | Above pipeline-side data engineering |
| India | ₹18L – ₹55L total | Product companies and GCCs building central data platforms |
The book for this field
Data Platform & Analytics
What the job actually is now, modelling and grain, data contracts and who gets paged, quality that is not a dashboard, streaming and when you need it, governance and the 82%, cost, and serving the AI workload.
Cross-cutting
Skills every archetype tests
These are shared across every field on this site — the same question asked in different vocabulary — so preparation here compounds rather than being spent once. The book above is specific to your field.
Practice questions across all themes → · Back to Data Platform & Analytics →