Data / Software Integration Engineer

Data / Software Integration Engineer

3 year W2 Contract · Connecticut · 2–3 days on site · Drug Discovery

• A pharma biologics discovery group is running a large-scale automation programme that is changing how they discover drugs. They are bringing roughly twenty pieces of integrated laboratory hardware online. Each one produces data in its own format, through its own software.
• Somebody needs to build the connective tissue: getting information out of that equipment and into the databases, scheduling software and analytical platforms where scientists can actually use it.

This is a software engineering role that happens to sit next to a lab. You do not need laboratory automation experience. You do need to be a real software engineer, and you do need to be willing to walk into a wet lab and work out how a liquid handler logs a barcode scan.

• What you'll be building
• Instruments generate data constantly - a robot moves liquid between plates, scans a barcode, logs what it did, and exports a file at the end of the run. Twenty instruments means twenty variations on that, none of them designed to talk to each other.
• Your job is to pull it together and route it where it needs to go:
• Instrument to database - reliable acquisition, transformation and storage of experimental data
• Instrument to scheduling software - ordering runs, and surfacing errors when something fails mid-run
• Instrument to the analytical environment - getting data to scientists in a form they can process, and to engineering for system monitoring

You will have a lot of autonomy over how this gets built. The architecture is not decided. Setting the pattern for how this integration works is the job, not implementing someone else's design.

Year one, in one sentence: a working connection from lab equipment through to the database, the instrument scheduling software, and the internal analytical ecosystem.

How the team works

• You report to the Automation Programme Lead and sit inside a small cross-functional group:
• A senior automation specialist who owns the physical hardware
• A project manager covering programme scope
• Likely a second contractor alongside you
• The internal IT and digital platform teams, on integration into the wider ecosystem
• Software practice is properly run an internal platform, standard Git workflow, CI/CD pipeline, three-week sprints, Scrum. You will be expected to work that way, not around it.

Roughly a quarter of the role is stakeholder-facing.

What we're looking for

• Software engineering, done properly. Not scripting. Product development discipline - version control, CI/CD, code review, working in sprints.
• Python, and comfort in whatever else the problem needs.
• Data pipeline and integration work - APIs, file parsing, ETL, moving data between systems that were never designed to talk to each other.
• High learning agility - More important than any specific technology on this page. You will be handed unfamiliar equipment, undocumented export formats and vendor software you have never seen, and asked to make sense of it. If that sounds like the interesting part rather than the annoying part, this is your role.
• Willingness to be physically in a laboratory. Two to three days a week on site, because the equipment is there and you cannot integrate what you cannot see. For the right person this can flex down to one or two.
• The soft side. Scientists are process-driven people, and this programme changes how they work. You need to bring them along rather than hand them a solution. Being able to sit with someone's reservations about a new system, and work through them, matters as much as the code.

• Helpful, but genuinely not required
• A scientific background - enough to follow a conversation with a scientist without needing everything translated
• ELN or LIMS exposure (Benchling, IDBS or similar)
• Any prior contact with laboratory instrumentation or automation
• Cloud data platforms, SQL, data warehousing

• Why it's worth doing
• Every large pharma is chasing "lab in the loop" right now. This group is genuinely doing it - building the automation and data foundation that makes in-silico drug discovery possible, on a multi-million-dollar programme with real hardware already arriving.
• The integration layer is the part nobody has solved yet, and whoever builds it here sets the pattern.