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Location: Ghent, Belgium. ONTOFORCE helps life sciences organizations accelerate research and drug development by unlocking hidden insights from complex data. Our flagship platform, DISQOVER, is a life sciences data and knowledge platform built on semantic technology and knowledge graph foundations, designed to connect siloed internal, licensed, and public data in one searchable environment. Why this role exists DISQOVER is only as good as the data beneath it. A large part of that data comes from the public life sciences landscape: clinical trial registries, chemistry and target databases, scientific literature, regulatory sources, ontologies, and terminologies. These are dozens of sources, each with its own release cadence and schema conventions, and each capable of changing without notice. Ingesting these sources is the straightforward part. The greater challenge is making them agree with one another. The same gene, drug, disease, or organization may be named several different ways across sources, yet customers expect to search once and find all of it. Mapping those references, harmonizing them, and correcting the semantic meaning behind them is central to what DISQOVER does. It is what separates a search engine over documents from a genuine discovery platform. This role owns the machinery that produces this, not simply keeping it running, but making it predictable, observable, and trustworthy enough that customers never have to think about it. What you'll own The public data pipelines, end to end. You will be accountable for our public feeds landing correctly and on time, release after release. That includes the less visible work: handling failures, managing reprocessing, and responding when a source changes its export format without warning. It also brings you closer to the harmonization than it might sound. A great deal of the mapping and correction logic lives inside these pipelines, so improving how a source is ingested directly improves how well DISQOVER resolves meaning across all of them. Keeping the lights on and moving the needle are the same work here, not competing priorities. An early-warning system for upstream change. Today we detect these changes manually. You will help us find better and faster approaches: schema drift detection, statistical checks on volumes and distributions, diffing across releases, and monitoring source announcements. A change upstream should surface as a signal in our systems, not as a question from a customer. Quality as an engineering practice. Our data work does not yet have the testing and validation discipline we apply elsewhere in engineering, and we want to change that. You will define what "good" means for a feed, make it measurable, and ensure it is enforced automatically. You choose the approach and see it through, and what you decide becomes how the team works. Bringing the outside in. We run most of our transformation logic on an ETL engine we built in house, and the reason is provenance. It tracks where every value came from at column and cell level, which the mainstream stack does not offer without significant effort, and which matters a great deal to customers who need to trace a conclusion back to its source. That requirement is why we built rather than bought, and it is why the decision has held. It also means we cannot simply adopt tooling that treats lineage as an afterthought. We are looking for someone who brings real experience of the modern technical data stack and can tell us where the ecosystem has caught up with us, where it has not, and which ideas are worth borrowing even when the tools themselves are not. We value a clear argument and a migration path over a preference. That perspective is also market intelligence: our customers' data teams work with these tools, and their expectations should help shape where DISQOVER integrates. How we work ONTOFORCE is small enough that individual contributions are visible and genuinely matter. With that comes real ownership, alongside the expectation that you pick up what needs picking up and help a colleague past a blocker. Teamwork is not a slogan here; it is how the work gets done. This role suits someone who wants a say in how the team operates, rather than a tightly scoped remit with little contact beyond it. You will join a team that already runs this stack. You are not inheriting an abandoned system, and you will not be the only person who understands it. We use AI coding assistants daily and expect you to as well. What interests us is not enthusiasm but judgement: knowing where they accelerate the work, where they produce confident but incorrect output, and how you tell the difference.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.