Backed by Battery and GV.
About the role
As a Staff Data Engineer on the Migrations team , you are the end-to-end technical and operational owner of client migrations. You will drive the full lifecycle, from deep-dive legacy analysis to production deployment, while orchestrating collaboration across Operations, Client Success, Product, Data Science and Platform Engineering. You don't just implement; you own the outcome.
What they're looking for
- Drive ambiguity to resolution — spot the unknowns early, own the open questions, and pull answers from clients, SMEs, and Operations instead of waiting to be unblocked
- Prove correctness at scale — build automated reconciliation frameworks that confirm, with confidence, that migrated output matches the source down to the row
- 8+ years a s a hands-on Data Engineer or Software Engineer, with demonstrated experience independently owning complex, multi-stakeholder technical projects from start to finish
- Strong Python and SQL — fluent with complex, unfamiliar legacy code, not just greenfield work
- Apache Spark — deep understanding of distributed processing, performance tuning, partitioning, and debugging at scale
- Apache Airflow — advanced, comfortable designing and authoring production DAG architectures from scratch
More about this role
Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.
As a Staff Data Engineer on the Migrations team , you are the end-to-end technical and operational owner of client migrations. You will drive the full lifecycle, from deep-dive legacy analysis to production deployment, while orchestrating collaboration across Operations, Client Success, Product, Data Science and Platform Engineering. You don't just implement; you own the outcome.
This is a high-trust, high-autonomy role for someone who can think like an architect, execute like a senior engineer, and coordinate like a program manager — all on the same project. You'll own the problem...
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