Senior Data Engineer

serif health • United State
Remote
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AI Summary

Serif Health is seeking a Senior Data Engineer to build and maintain their data platform and data products. The ideal candidate will have expertise in Databricks/Spark, Snowflake, and data orchestration tools. They will design thoughtful data models, identify areas for optimization, and balance tradeoffs between cost, performance, and development speed.

Key Highlights
Design and implement data models for Serif Health's data products
Develop and maintain the Serif Health data platform
Balance tradeoffs between cost, performance, and development speed
Technical Skills Required
Databricks Spark Snowflake Airflow Python SQL
Benefits & Perks
Remote work
Quarterly company gatherings in rotating locations across the U.S.

Job Description


About Serif Health

At Serif Health, we are taking the complexity out of the cost of healthcare. Our mission is to make healthcare pricing and contracting more transparent, easier to understand, fairer, and ultimately more affordable for all. New price transparency regulations have made a wealth of healthcare pricing and reimbursement data publicly available for the first time, but it is still extremely hard to ingest, organize, and actually use this data to understand the cost of care. That’s where we come in.

Serif Health is building healthcare price intelligence data products and APIs to power the future of more transparent healthcare. Our world-class team aggregates, enhances, and distributes trillions of healthcare pricing data points each month for a growing list of B2B & Enterprise customers. Thanks to our tight focus and high level of expertise, we have strong customer traction, high NPS and inbound referral rates, and a top 1% balance sheet.

We’re looking for a Senior Data Engineer who will help us build and maintain our data platform and data products to empower our customers to derive meaningful insights for their transparency needs.

What You’ll Do

  • Own the development and maintenance of the Serif Health data platform which includes the data warehouse/lakehouse and orchestration pipelines
  • Design thoughtful data models for Serif Health’s suite of data products that help effectively answer end user’s questions
  • Identify areas for further optimizing existing data pipelines or products
  • Partner with internal stakeholders to understand complex customer or product requirements; creatively devise solutions to achieve them
  • Balance the tradeoffs between cost, performance, complexity, maintainability, scalability, and development speed when implementing data products and pipelines
  • Keep abreast of industry developments and best practices in data operations engineering

What We Expect

  • Deep expertise with Databricks/Spark, Snowflake and similar distributed data warehousing tools.
  • Experience with data orchestration tools such as Airflow, Prefect, Dagster or similar ETL orchestration tools
  • Strong Python and SQL skills
  • Comfort with efficiently handling large data volumes (50TB+, 500BB+ rows added monthly)
  • Strong product intuition and empathy for end users; ability to translate complex user questions into data assets
  • High attention to detail, excellent written and verbal communication skills, and professionalism.
  • Ability to work independently in a fast-moving, distributed team environment.

What We'd Love To See (Bonus)

  • Experience with healthcare data: medical claims, prescription drugs, life sciences, procedures pricing or billing.
  • Prior experience in data science, machine learning, or statistics
  • Prior commercial experience in a customer success engineering or data operations role working directly with enterprise customers
  • Prior work in a startup or high-growth software company environment.

Job Location: Anywhere in the U.S. - Serif Health is a remote-first company with team members across all US time zones. We hold company gatherings at least quarterly in rotating locations across the U.S. for team members to get to know each other and collaborate in-person.

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