About the role
As a Senior Data Engineer, you will own NimbleRx’s data platform — the layer that every other team builds on to understand the business, ship product features, and meet our compliance obligations. The work scales beyond you: what you build once accelerates product, analytics, ops, and data science for years.
The role lives at the intersection of production engineering and data engineering, and the test of your work is whether other teams move faster because of it. This is an opportunity to be rewarded for hard technical work on a problem that genuinely matters.
Our engineering culture: We operate with shared trust and no egos. We enjoy being “in this together” and collaborating on the challenges of a rapidly scaling business. We live out our company values of curiosity, ownership, simplicity, and a get-it-done mentality.
We’re open to Senior or Lead candidates. What matters is what you could build, not your current title.
What you'll do
- Own the data platform end-to-end — ingestion, transformation, storage, query, and access — and drive its roadmap as the company’s data needs grow.
- Build and evolve batch and streaming pipelines on PySpark/EMR, Kinesis, Lambda, and Step Functions, ingesting from Postgres, Salesforce, third-party vendors, and product event streams into our Iceberg-based lake.
- Model the warehouse: design SCD tables, event tables, and the conventions other engineers and analysts follow when adding new data.
- Partner with product, engineering, analytics, and operations stakeholders across the company to turn data requests into well-scoped, reliable pipelines — and write the docs and tooling that let them self-serve next time.
- Own the security and compliance backbone of our data systems, including audit logging, access control, and temporary-access workflows.
- Optimize backend query performance where the data layer meets product code — read-replica routing, indexing, caching, and IO instrumentation in our Java/Spring services.
- Lead investigation and remediation when data infrastructure misbehaves — IOPS spikes, pipeline failures, schema drift, late data — and make the fixes durable.
- Use AI as a daily accelerant for pipeline scaffolding, schema work, and ad-hoc investigations — and ship internal AI tooling that lets other teams do the same.
- Mentor engineers and analysts across the company on how to work with the data platform.
Qualifications
- 5+ years of experience building production data pipelines and platforms
- Deep Python (PySpark) and SQL fluency, including tuning Spark jobs at scale
- The skills and willingness to work on the services around the data layer (Java, Spring Boot)
- Hands-on experience with distributed compute (Spark/EMR), streaming (Kinesis), and object storage (S3)
- Solid Postgres fundamentals — query optimization, indexing, replication, replica routing, and a feel for when the database is the bottleneck
- Experience with Iceberg and Trino, or similar
- Comfort with CI/CD and Terraform
- Already building with AI — frontier models, agentic coding tools, or something you hacked together last weekend
- Track record of working across teams that don’t speak your language (product, ops, etc.)
About you:
- You take ownership of the platform’s reliability, cost, accessibility, and compliance — not just the tickets you happened to ship.
- You think about security and PII/PHI handling as core engineering work, not as someone else’s problem.
- You’re a force multiplier: the docs, tools, and skills you leave behind make other engineers and analysts faster long after the original ticket closes.
- You have a bias for shipping iteratively and instrumenting what you ship — you’d rather demo a rough v1 in two days than a polished v3 in two weeks.
- You take pride in your work and have good judgment about what to prioritize when everything feels urgent.