Senior Data Engineer

Luna Health•San Diego, CA

9d•Hybrid

About The Position

Luna Health created Sleep-Only Automation™, a medical device startup focused on providing nighttime glucose control for Type 1 and Type 2 diabetes insulin users. We offer the world's smallest insulin patch pump designed for individuals who manage their days well and prefer to stay on pens, needing only nighttime protection. Our founders, with personal experience in diabetes, are building an innovative solution to an unmet need in the industry. This role involves owning Luna's data architecture end-to-end, reporting to the Director of Data Science. As a startup, this position requires wearing multiple hats, contributing to product concepts, and starting upstream with user needs to ensure the collection of relevant data. You will translate clinical, regulatory, and commercial needs into instrumentation specifications, collaborate with data science, engineering, and operations for implementation, and build the infrastructure to make the data useful. The data is diverse and complex, including telemetry from embedded and mobile systems with intermittent connectivity, clinical outcomes, EDC data, and evolving manufacturing systems, all with commercial and regulatory implications. This is a hands-on role requiring architectural thinking, independent execution, and a commitment to building robust systems.

Requirements

7+ years of data engineering experience with a track record of owning and delivering production data systems end-to-end.
Experience understanding user needs, defining data models, and establishing instrumentation requirements upstream of pipeline development.
Strong proficiency in Python and SQL.
Working knowledge of cloud data platforms (Google Cloud, BigQuery, Firestore mentioned).
Strong data modeling skills, and the judgment to serve multiple consumers and make tradeoffs explicit.
Experience working across teams (e.g., data science, engineering, operations, clinical, regulatory) to translate business and scientific requirements into specifications.
Clear, direct communication with non-technical stakeholders.
Must live within a reasonable commuting distance to San Diego office for onsite/hybrid work (3+ days/week).
Authorized to work for any employer in the U.S.

Nice To Haves

Experience in a regulated industry (medical devices, pharma, clinical research) with working knowledge of what auditability and traceability require in practice.
Familiarity with CDISC standards (SDTM/ADaM) or clinical trial data formats.
Experience with EDC systems (e.g., Castor, REDCap, Medidata Rave, Veeva Vault).
Exposure to 21 CFR Part 11 requirements for electronic records and audit trails.
Experience with manufacturing, supply chain, or operational data systems.
Background in medical devices, diabetes technology, or digital health.
MS in computer science, data engineering, biomedical informatics, or a related field.

Responsibilities

Define, with the Director of Data Science, what data Luna needs to collect from the device, from manufacturing, and from business operations.
Translate those requirements into instrumentation specifications and data models, and work with software engineering and operations to see them implemented correctly.
Design and own the data schemas that serve analytical, regulatory, and operational consumers, making deliberate tradeoffs and documenting the reasoning behind them.
Build models that accommodate and inform how the business plan will evolve.
Build and maintain transformation pipelines on Google Cloud and BigQuery that turn raw cloud data into analysis-ready datasets.
Own the warehouse layer: schema implementation, versioning, access controls, and data quality monitoring.
Keep lineage and transformation logic traceable to a standard that survives regulatory audit.
Build and maintain integrations with clinical data sources, including EDC systems and third-party device data.
Produce CDISC-compliant datasets (SDTM/ADaM) for regulatory submissions.
Build the data infrastructure for manufacturing as it scales: lot tracking, supply chain, process control metrics, and non-destructive testing records.
Work with manufacturing and quality to determine what process data needs capturing, and build the systems that capture it reliably.
Define the data quality metrics, build the checks, and own the resolution when something looks wrong.
Do the analysis when the team needs it, moving between infrastructure and analytical work as priorities demand.
Build internal tooling and documentation that makes data accessible and trustworthy for clinical, regulatory, and operational stakeholders.