Senior Data Platform Engineer Jobs — Live Roles

A quick honesty note before the listings: "Data Platform Engineer" isn't a separate category in the index's role taxonomy — these titles are captured under the broader Data Engineer tag, filtered to Senior-level postings. If an employer's title says "Data Platform Engineer" specifically, it still shows up here; the filter just doesn't distinguish it from a "Data Engineer" or "Data Infrastructure Engineer" title at the same seniority level.

Senior data platform postings tend to ask for ownership of the systems other engineers build on top of — the pipelines, the warehouse, the orchestration layer — rather than a single analytical project. The proof that differentiates a strong candidate here is reliability: an SLA you owned and the incident response behind it, a migration you drove between storage or processing systems, a cost or latency number you actually reduced and can explain how.

This is also a role family where the stack genuinely varies by employer — one posting wants deep Spark/Databricks experience, another wants dbt and a cloud warehouse, a third wants streaming infrastructure like Kafka or Kinesis. Don't pad a resume with every data tool you've seen once; name the specific platform you've operated in production and the scale (data volume, pipeline count, SLA) you were responsible for.

Before applying, run the match score against the specific posting. The tooling differences in this field are real enough that a generic "data engineer" resume often misses the specific platform experience a given posting is actually screening for — the gap analysis will show you exactly which requirements are covered and which are genuine gaps rather than let you assume adjacent experience counts.