Data Engineer

Function Health•US - Remote,

About The Position

Function Health is seeking a Data Engineer to join their team. The role focuses on building a platform that enables subject matter experts to safely and efficiently make changes to data, moving beyond traditional ticketing systems. This involves creating a 'paved road' with guardrails such as declarative contracts, data diffing CI, validations, lineage tracking, and rollback capabilities. The goal is to increase the speed at which both humans and agents can work with data without compromising validity or stability. This is a platform engineering role where the users are other engineers.

Requirements

Experience building internal platforms or infrastructure that other engineers have adopted.
Experience operating production data or ML systems, including being on-call and resolving issues under pressure.
Strong proficiency in Python and SQL.
Comfort working with lakehouse environments (Databricks experience preferred; Snowflake or BigQuery experience is transferable).
Experience designing and evolving interfaces and schemas that other teams depend on without causing breakage.
Demonstrated understanding of testing and CI for data or ML, particularly where correctness is statistical and failures can be silent.
1-4 years of engineering experience, with a focus on built work rather than tenure.

Nice To Haves

Declarative pipeline frameworks (e.g., dbt, DLT, Dagster, Airflow)
Experience with streaming technologies (e.g., Kafka, Spark Structured Streaming)
Familiarity with data contracts, data diffing, or lineage tooling
Experience with Terraform
Knowledge of feature stores
Experience with MLOps and evaluation tooling
Familiarity with agentic coding workflows
Experience in the healthcare industry
Experience with Protected Health Information (PHI) or HIPAA compliance

Responsibilities

Develop and maintain tracking infrastructure for the event pipeline supporting product analytics, experimentation, and feature gates, ensuring schemas are enforced at the source to prevent bad metrics.
Build and manage data processing infrastructure, including the Bronze → Silver → Gold layer in Databricks, with automated schema evolution, contract tests, reliable backfills, and freshness/volume monitors derived from contracts.
Implement and support ML infrastructure for feature computation and serving, training and evaluation pipelines, and integrating model outputs back into the product with robust testing and observability.
Enhance the self-service capabilities across all three areas, providing templates, local development and preview environments, policy-as-code for PHI, ownership routing for alerts, and progressive gates for different levels of scrutiny.