Data Engineer

Function Health is building platform engineering that helps engineers and agents make safe, fast changes across data, analytics, and ML. You’ll focus on infrastructure that strengthens correctness with testing and validations, improves traceability with lineage, and reduces operational risk with rollback-ready workflows. The work is remote, with a clear emphasis on member impact, execution, and standards.

What you’ll build

• Event tracking infrastructure for product analytics, experimentation, and feature gates, with schemas enforced at the source so incorrect events do not become incorrect metrics.
• Databricks lakehouse pipelines across Bronze, Silver, and Gold, including automated schema evolution, contract tests, and backfills designed to be manageable. You’ll also generate freshness and volume monitors from the contract rather than adding them later.
• Feature computation and serving, plus training and evaluation pipelines, along with the plumbing required to bring model outputs back into the product using the same testing and observability expectations applied to the rest of the system.
• A self-service platform experience with templates, local development, and preview environments, including policy-as-code for PHI, ownership routing for alerts, and progressive gates that enable exploratory models to ship freely while member-facing models receive additional scrutiny.

How you’ll work

This role is built for someone who has shipped internal platforms that other engineers actually adopted. You’ve experienced the difference between creating a tool and making it part of how teams deliver. You’ll operate production data or ML systems, respond on call, and fix issues under pressure.

Core requirements

• Strong Python and SQL, with comfort working in a lakehouse. Function Health uses Databricks (experience with Snowflake or BigQuery translates well).
• Experience designing interfaces and schemas that other teams depend on, and evolving them without breaking downstream consumers.
• Thoughtful testing and CI practices for data or ML, where correctness is statistical and failures can be silent.
• 1 to 4 years of engineering experience (what you built matters more than the number).

Tools you may use

• Databricks, Snowflake, BigQuery
• dbt, DLT, Dagster, Airflow
• Kafka, Spark Structured Streaming
• Python, SQL, Spark
• Terraform

Nice-to-have

• Declarative pipeline frameworks: dbt, DLT, Dagster, Airflow
• Streaming experience: Kafka, Spark Structured Streaming
• Data contracts, data diffing, or lineage tooling
• Feature stores, MLOps and eval tooling, agentic coding workflows
• Healthcare experience, especially PHI or HIPAA

Values that guide the work

• Ruthless Prioritization to move quickly and keep standards high.
• Member-First, Always with responsiveness, peace of mind, and outcomes.
• One Team, Moving Fast with clear communication and alignment.
• Radical Ownership, Relentless Execution with urgency, precision, and follow-through.
• Mission Over Ego with honesty, transparency, and respect.
• Sustained Integrity in Every Detail, including clinical precision and accuracy.