Senior Data Engineer- Data Platform

Vida is looking for a Senior Data Engineer to help shape the data platform that supports much of Vida's business. Our team builds and operates the systems that ingest, validate, transform, and serve data across Vida. We work with data from health plans, employers, clinical programs, and internal systems across member identity, eligibility, claims, billing, reporting, and analytics. You will own data systems and domains end to end, from ingestion and validation through storage, transformation, and delivery. You will make architecture decisions, change production systems safely, and help other engineers make good decisions about data infrastructure.

Responsibilities:

• Own production data systems and domains from ingestion through delivery.
• Design ingestion patterns that handle incomplete, duplicated, delayed, malformed, and changing partner data.
• Define the contracts between source systems, ingestion, transformation, and downstream consumers.
• Design data models with clear grain, keys, history, and definitions so the same question doesn't get two different answers.
• Evolve schemas, pipelines, and datasets safely, with retries, backfills, and replay that don't corrupt or duplicate data.
• Improve pipeline performance, observability, and failure handling so you can tell when something is wrong and why.
• Work with Engineering, Product, Operations, and Analytics to figure out what the data needs to represent when requirements are incomplete or messy.
• Additional responsibilities as needed.

Qualifications:

• Bachelor's degree at a minimum.
• 5+ years building production data systems.
• Strong SQL and data modeling fundamentals, including grain, keys, history, dimensional modeling, and the tradeoffs between different representations of the same data.
• Experience designing and operating batch and asynchronous data pipelines, including the failure modes that appear in distributed systems.
• Experience with a cloud data platform and an understanding of storage, query performance, and cost.
• Strong instincts around data correctness, including validation, idempotency, late and duplicated records, schema changes, backfills, and reprocessing.
• Working proficiency in Python for pipelines, services, tooling, and tests.
• The ability to change production systems in ordered, reversible steps without breaking their consumers.
• Clear written communication, including documenting technical decisions and explaining changes to people outside of engineering.
• You do not need previous healthcare experience or experience with every technology we use.