Senior Data Engineer

About the role

Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.

Machinify builds AI-powered software that helps health plans pay claims accurately. As a Senior Data Engineer, you will design, build and run the data pipelines and models behind our payer-facing products, and help bring legacy data processes over into a common platform. You'll extend and integrate tooling along the way so the results can be reused by partner teams, not just our own.

This is a broad engineering role. You'll own pipelines end to end: ingestion, modeling, quality, deployment and day-to-day operations, in an AI-assisted engineering environment.

What You’ll Do

- Design, build and operate reliable, automated data pipelines for health plan data.

- Model complex, messy healthcare data into clear, trustworthy canonical and analytical models.

- Bring legacy processes, pipelines and models over into a common platform, preserving the logic and behavior that already work.

- Extend and integrate tools and workflows so other teams can adopt them.

- Build in data quality and software engineering discipline: validation, automated testing, code review, CI/CD.

- Document what you build and review AI-generated work critically.

What You Bring

- 5+ years in data engineering, including production ownership of the pipelines you built.

- Strong SQL and Python.

- Strong data modeling: canonical and dimensional design, conflicting sources, history, adjustments. You can explain why a model is shaped the way it is.

- Production dbt and workflow orchestration (Airflow or similar).

- Expertise with Snowflake and/or Databricks, Spark SQL experience

- Data quality and CI/CD practices applied to data: validation and reconciliation, pull requests, automated tests, deployment automation.

- Generalist range: you can take a broad, loosely defined problem and run it end to end across ingestion, modeling, infrastructure, APIs and operations, including incident response, and you onboard a new data source from discovery to delivery.

- A record of extending or integrating existing tools and building things other teams use.

- Quick, deep learning of complex domains: you ask why the data looks the way it does, and you verify AI-generated code and abstractions against ground truth.

- Good intuition on how to leverage AI in development and automation in day-to-day engineering practices, understands how to leverage AI as a force multiplier for efficiency, and potential risk