Senior Data Engineer

Luna Health • San Diego, CA

11d • Hybrid

About The Position

Luna Health created Sleep-Only Automation™: insulin delivery that works while you sleep and disappears when you wake. We're a venture-backed medical device startup that makes the world's smallest insulin patch pump, designed to provide nighttime glucose control for Type 1 and Type 2 diabetes insulin users who manage their days well and have made a considered choice to stay on pens. They don't need a full replacement of their chosen therapy, they need protection while they sleep.
Our founders have lived this problem as people with diabetes and as parents of children with diabetes, and they have built some of the most recognized products in the space. Now we are building the answer to a question the industry left unanswered: what do pen users need at night that no one has built yet. Sleep-Only Automation™ is it.

The Role
You will own Luna's data architecture end-to-end, reporting to the Director of Data Science. Plus at a startup you will wear multiple hats, and you will be an integral contributor (and sometimes owner) to the product concepts we explore and develop.
This role starts upstream, starting with user needs and ensuring that we're collecting the right data in the first place. You will turn clinical, regulatory, and commercial needs into concrete instrumentation specifications, work with data science, engineering, and operations to get them implemented, and then build the infrastructure that makes the resulting data useful.
The data is varied and complex. Telemetry data from embedded and mobile systems with intermittent connectivity, clinical outcomes with health implications, EDC data, evolving manufacturing and production systems. All have both commercial and regulatory implications, and several you will be building from scratch.
This is a hands-on role. You will design schemas and implement them, sit in planning conversations because your technical perspective improves the decision, and then go build what was decided. We are looking for someone who thinks architecturally, executes independently, and takes real satisfaction in building things that hold up under scrutiny.
This is an onsite/hybrid role (3+ days/week) in our San Diego office, and you must live within a reasonable commuting distance.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.

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. We run Google Cloud, BigQuery, Firestore; orchestration tooling is a choice you’ll make
• 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

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. You will move 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

Benefits

• A no-jerk policy — we seek the best and brightest talent who are fun to work with
• Equity — we are just setting off on this journey, and you will get in from the start
• Excellent health benefits — we depend on it ourselves as a team that has or cares for people with diabetes

What This Job Offers

• Job Type: Full-time
• Career Level: Senior