Data Engineer

• At Function, the person who best understands a piece of data is rarely the person who can safely change it
• A data scientist knows exactly how a biomarker should be derived
• A clinician knows which reference range is wrong
• Today, both have to file a ticket and wait for an engineer
• We think that’s a platform failure, not a process problem
• Our job is to build the paved road that lets the subject matter expert make the change themselves – and lets an agent make it too – without anyone lying awake wondering about the validity or stability of what just shipped
• That means the guardrails have to be real: declarative contracts instead of hand-rolled code, CI that diffs the data and not just the diff, validations that gate promotion, lineage that tells you who’s downstream, and a rollback that takes one command
• Get that right and both your humans and your agents get faster at the same time, for the same reason. That’s the work. It’s platform engineering, and the users are engineers
• Three surfaces, all with the same goal – make the safe path the fast path:
• Tracking infrastructure. The event pipeline behind product analytics, experimentation, and feature gates. Schemas that are enforced at the source, so a bad event never becomes a bad metric
• Data processing infrastructure. The Bronze → Silver → Gold layer in Databricks. Automated schema evolution, contract tests, backfills that aren’t scary, freshness and volume monitors generated from the contract rather than bolted on after
• ML infrastructure. Feature computation and serving, training and eval pipelines, and the plumbing that gets model output back into the product — with the same testing and observability bar as everything else
• Cutting across all three. The self-service story. Templates, local dev and preview environments, policy-as-code for PHI, ownership routing for alerts, and progressive gates so an exploratory model ships freely while a member-facing one earns more scrutiny

Benefits

• 401k Match
• Choose your own holidays
• Health Insurance

• Designed interfaces and schemas that other teams depend on, then evolved them without breaking those teams
• Built internal platform or infrastructure that other engineers actually adopted. You’ve felt the difference between shipping a tool and getting it used
• Thought hard about testing and CI for data or ML, where correctness is statistical and the failure is often silent
• Operated production data or ML systems, on call for them, and fixed them under pressure
• Strong Python and SQL. Comfortable in a lakehouse – we use Databricks; Snowflake or BigQuery translates fine
• 1-4 years of engineering experience gets you here, but we care about what you’ve built, not the number
• Declarative pipeline frameworks (dbt, DLT, Dagster, Airflow)
• Streaming (Kafka, Spark Structured Streaming)
• Terraform
• Data contracts, data diffing, or lineage tooling
• Feature stores
• MLOps and eval tooling
• Agentic coding workflows
• Healthcare
• PHI, or HIPAA experience