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Staff Data Engineer, Lab Data Platform

Asimov

Boston, MA · HybridFull-time$200–235K/yrPosted todayStill listed today

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At a glance

Compensation
$200–235K/yr
Location
Boston, MAHybrid
Schedule
Full-time
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Job overview

Asimov is seeking a Staff Data Engineer to own the path from laboratory data capture through the warehouse and into its Kernel platform and models. The role combines data and software engineering, working closely with laboratory and computational teams to standardize experiment records, build traceable data pipelines, model biological processes, and deliver datasets, APIs, reporting, and analysis tools that support scientific design.

Skills & qualifications

RequiredNice to have

Skills

Data Platform Architecture, nice to havePython, nice to haveSQL, nice to haveBackend Services, nice to haveAPI Development, nice to haveBiotech Wet Lab, nice to haveData Modeling, nice to haveData Pipelines, nice to haveData Ingestion, nice to haveData Quality, nice to haveData Lineage, nice to haveData Versioning, nice to haveFAIR Data Stewardship, nice to haveAgentic Reporting, nice to haveData Analysis, nice to haveDashboard Development, nice to haveAmbiguity Management, nice to haveScientific Collaboration, nice to have

Qualifications

8+ Years Production Data Systems, nice to haveData Platform Architecture Ownership, nice to have

Full job description

Asimov's mission is to advance humanity's ability to design living systems. We build mammalian cell lines, genetic parts and lentiviral systems in our Boston lab, and we build Kernel, the model-driven CAD platform that turns that lab's knowledge into software our partners use to design next-generation therapeutics.

We've built a best-in-industry dataset for mammalian cell line development: thousands of antibodies, plasmids, genetic elements, pools and clones, each linked to the measurements that show how they actually performed in the lab. That data already powers Kernel's design recommendations and our predictive models, and it's a big part of why partners choose us.

Now we want to take it to the next level of maturity. That means capturing every instrument’s data from every experiment the same way, at higher throughput, continuously improving in quality and depth of analysis, fully traceable to the designs and conditions that produced it and ready for the next generation of models. We're looking for one person to own that path from bench to warehouse to model: half data engineer, half software engineer, with your feet in the lab.

About the Role:

  • Standardize how the lab captures data. Sit with our cell line development, analytics, and molecular biology teams to define how samples, conditions and measurements are recorded, then build the ingestion tooling that makes the right way the easy way.

  • Own the lab data platform end to end. Design and run the pipelines that move data from instruments, our ELN/LIMS and bench workflows into our warehouse (BigQuery) and into Kernel, with quality checks, lineage and versioning built in. Embrace FAIR principles in data stewardship.

  • Model the biology and the process. Build schemas that link a molecule design to its constructs, the cell pools and clones that carry them, the bioprocess, and every titer, quality, and stability measurement taken along the way.

  • Feed the models and the product. Deliver clean, ML-ready datasets to our modeling and computational biology team, and ship APIs and services that surface lab results inside Kernel and to KernelAI, our agentic design assistant.

  • Enable the science. Build deterministic and agentic reporting and analysis tools and dashboards side by side with computational and data scientists to close the loop from data back to design input.

About you:

  • You have 8+ years building production data systems (5+ for Senior), and you've owned a data platform's architecture, not just its pipelines.

  • You write strong Python and SQL, and you're comfortable shipping backend services and APIs, not only batch jobs.

  • You've worked in or alongside a biotech wet lab, and you know why lab data gets messy. You'd rather walk into the lab and watch an experiment than guess at requirements.

  • You can take an ambiguous, first-of-its-kind problem and turn it into a system scientists actually adopt.

We're a small team where software engineers and biologists share a floor, and your work shows up in real experiments within weeks, not quarters. We value velocity, autonomy and concrete outcomes over process. If you've ever wished lab data just made sense, come build the system that makes it so.

We’re fueled by a vision to transform biological engineering into a fully-fledged engineering discipline. Should you join our team, you will grow with a constantly evolving organization and push the frontiers of synthetic biology. Company culture is key to Asimov, and we believe that our mission can only be achieved by bringing together a diverse team with a mixture of backgrounds and perspectives.

How this job compares

Set against the 6,255 open Data Engineer jobs on Olive Jobs, counted November 10, 2026.

Pay
  • This posting$200–235K/yr
  • Massachusetts median$140–208K/yr135 salary ranges
  • US median$125–208K/yr4,857 salary ranges

This range's midpoint, $218K, is 28% above the Massachusetts median midpoint ($170K) — in the top quarter of Data Engineer salary ranges in Massachusetts.

Openings
Olive Jobs lists 160 open Data Engineer jobs in Massachusetts and 6,255 across the US. 8% of them nationwide are listed as remote or flexible.
Hiring the most in Massachusetts
Experience
Data Engineer postings that state a minimum ask for 5 years of experience at the median; the middle half ask for 3 to 7 years (5,438 postings).
Education
2,380 of the 6,255 open Data Engineer jobs (38%) ask for a bachelor's degree or higher.

Counted across the open, public US postings on Olive Jobs on November 10, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.

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