The Senior Research Data Engineer will architect data pipelines, manage data transformation, and collaborate with product and analytics teams to enhance health products.
We’re looking for a Senior Research Data Engineer who’s eager to shape the foundation of data at Paceline. You’re someone who thrives in a startup environment where you can blend strategy with hands-on execution, laying the groundwork for data systems that fuel meaningful insights in health and insurance.
At Paceline, data is at the heart of how we unlock new possibilities in human health. You’ll partner closely with product, analytics, and engineering to transform wearable data and real-time analytics into products that help people live healthier lives.
Role Responsibilities
- Drive impact by architecting the pipelines and infrastructure that power our research-driven health products.
- Own the design and development of both real-time and batch data systems, from event ingestion to analytics-ready datasets.
- Manage and transform raw data streams from wearables, APIs, and internal tools into structured, reliable sources of truth.
- Build scalable backends and data services that deliver insights seamlessly across our platform.
- Improve reliability and observability through robust monitoring, logging, and automated backfills.
- Establish and maintain a living data knowledge base to enhance discoverability, documentation, and cross-team collaboration.
- Work closely with product and analytics teams to enable experimentation and faster iteration.
- Lay the foundations for a future data organization, setting standards, processes, and systems that scale.
Preferred Qualifications
- 3–5 years of experience as a Data Engineer or Backend Engineer with a strong data focus.
- Proficiency in Python and SQL, with proven ability to work efficiently with large datasets.
- Hands-on experience with cloud platforms (AWS preferred) and working directly with raw, messy data.
- Understanding of streaming systems and real-time data processing.
- Familiarity with modern data stack tools such as dbt, Airflow, Spark, or Databricks.
- Strong grasp of data modeling, warehousing, and distributed systems.
- Pragmatic approach to building—balancing scalability, speed, and simplicity.
- Clear communicator who can articulate technical trade-offs to both technical and non-technical teammates.
- Experience with digital signal processing or time-series data.
- Background in experimentation frameworks and data-driven product development.
- Prior startup experience, with a comfort level in ambiguity and ownership.
At this point, we hope you're feeling excited about the job description you’re reading. Even if you don't meet every single preferred qualification above, we still encourage you to apply.
We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways - not just candidates who check all the boxes.
Top Skills
Airflow
AWS
Databricks
Dbt
Python
Spark
SQL
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