TalentfindersHiring data platform engineers for games and AI teams
Data platform roles get written as analytics jobs and filled by analytics people. Here is how to brief and screen for the engineering version.
Data platform roles are among the most commonly mis-briefed engineering jobs. Written loosely, the posting attracts analysts; written well, it attracts infrastructure engineers who happen to work with data.
What does the role own?
A data platform engineer owns ingestion, storage, orchestration, and data quality: the infrastructure that analytics and machine learning both depend on. Success is measured by whether downstream consumers can trust the data and whether pipelines recover without manual intervention. It is an infrastructure role, and it should be briefed and compensated as one.
What should the brief specify?
Specify the scale, the stack, and the consumers. "Data engineer wanted, SQL and Python" describes a hundred different jobs at wildly different levels. Naming the event volume, the warehouse, the orchestration tool, and whether the consumers are analysts or training pipelines lets a qualified candidate self-identify in one read.
In games specifically, telemetry volume is the number that matters most. A platform ingesting client telemetry from a live title operates at a scale that most business-analytics platforms never approach, and candidates from that world will recognise the problem immediately.
What should you screen for?
Screen for how they handle broken data, not clean data. Ask about a pipeline failure that produced wrong numbers rather than no numbers, since silent corruption is the failure mode that damages trust. Strong candidates describe detection, backfill strategy, and the contract or test they added afterwards to prevent recurrence.
Additional probes worth including:
- Schema evolution: how they changed a schema without breaking consumers.
- Late-arriving and out-of-order data, which is endemic in game telemetry.
- Cost management as volume grew.
- How they established data quality expectations with downstream teams.
Does the role need machine learning knowledge?
The role needs enough machine learning context to serve training and feature pipelines correctly, not the ability to train models. Understanding why training and serving skew matters, and what a feature store is for, is sufficient. Requiring modelling experience turns an infrastructure search into a scarce-specialist search without a corresponding benefit.
Why do these searches stall?
They stall when the brief attracts analysts and the loop tests infrastructure. Candidates arrive strong on SQL and analysis, fail a systems-design conversation, and the funnel produces volume with no fit. Fixing the brief resolves most of it, which is why the scorecard conversation at Talentfinders happens before any sourcing begins.
Frequently asked questions
What does a data platform engineer do?
A data platform engineer builds and operates the systems that move, store, and serve data for analytics and machine learning. They own pipeline reliability, schema evolution, and data quality. It is an infrastructure role measured by whether downstream consumers can trust the data, not by the analysis produced from it.
How is this different from a data engineer or analyst?
Analysts answer questions with data. Data engineers build pipelines that deliver it. Data platform engineers build and operate the infrastructure both depend on, including ingestion, storage, orchestration, and data quality tooling. The distinction matters because the three attract different candidates and different compensation.
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