Lakshya

Data Platform & Analytics

Data Architect

Data · owns the model, not the pipelines

Decisions that outlive the tooling. Judged on whether the platform is still usable in three years.

A data architect owns the shape rather than the plumbing: what the conformed entities are, where boundaries sit, which system is the source of truth for what, and which of the many overlapping tools actually get used. The decisions are long-lived, which is why the role is small and scrutinised.

The decisive round

“Centralised warehouse or data mesh?”

Interviewer: “We have twelve teams producing data and a central team that is a bottleneck. Which model?”

A weak answer. “Data mesh — decentralised ownership with domain teams owning their data as products, with a self-serve platform and federated governance.” The four principles recited. It does not engage with whether those twelve teams can actually carry ownership, which is the question that decides it.

A strong answer. “The bottleneck is real, and decentralising ownership only helps if the domain teams can carry it — otherwise you replace one slow team with twelve inconsistent ones and no way to join anything.

So I would ask three things first. Do domain teams have anyone who can own a data product, including on-call for freshness? Usually two or three do and the rest do not, and that asymmetry decides the design. Do we have a platform good enough that owning a data product is a reasonable ask rather than a research project? If not, mesh means every team builds their own pipeline badly. And who owns the conformed entities that cross domains — customer, account — because that is precisely where mesh is weakest and where the organisation's most important numbers live.

What I would actually do is neither, at first. Keep core conformed entities central and owned, because one definition of customer is worth more than any organisational principle. Push domain-specific marts out to the teams that understand them, with contracts and quality standards they must meet. And invest hard in the platform, because whether decentralisation works is almost entirely a function of how good the paved road is.

That is mesh-shaped in the places it works and centralised where it does not, which is what most successful implementations actually look like even when they use the vocabulary.”

Engaging with whether domain teams can carry ownership, and protecting the conformed entities. Reciting the four principles is the most common answer and the weakest.

  • Have a target-state diagram you drew and can defend, including what you left alone.
  • Have a source-of-truth decision you made where two systems both claimed it.
  • Know slowly-changing dimensions and grain cold, because they are what make the model survive.
  • Have a migration you finished, or an honest account of one that stalled.

Compensation

MarketBandNotes
United States$160k – $260k baseSmall population, long-lived decisions
India₹25L – ₹65L totalBFSI and large GCCs

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.

CHAPTER 2Modelling, and why it still decides everythingThe least fashionable skill in the field and the one that determines whether the platform is usable in three years.READ THE CHAPTER →CHAPTER 3Data contracts, and who gets pagedThe structural fix for the defining problem of the field: your pipeline breaks because of a change you did not make.READ THE CHAPTER →CHAPTER 8Serving the AI workloadAI appears in 92% of these postings. What it actually asks of a data platform is specific.READ THE CHAPTER →

All 8 chapters in Data Platform & Analytics →

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.

Discovery before solutionThe decisive round in three separate archetypes, with the lowest pass rate of any stage.READ THE CHAPTER →Design under constraintThe decisive round for SRE, AI Platform, Infrastructure — and the reason strong engineers fail it.READ THE CHAPTER →The controlOne habit separates strong candidates from plausible ones more reliably than any technical depth.READ THE CHAPTER →

Practice questions across all themes →  ·  Back to Data Platform & Analytics →