Senior Data Engineer

About Orakl Oncology

At Orakl Oncology, we are accelerating the development of oncology treatments. Today, fewer than 5% of new cancer drugs succeed in clinical trials. Clearly, new methods are needed. We combine cutting-edge biology and AI to build the next generation of insight platforms with the world’s largest cohort of patient tumor avatars. These avatars fuel our AI-powered predictive engine, helping to anticipate clinical trial outcomes, validate therapies, and uncover new drug candidates. By generating multimodal, real-world data, we deliver insights that consistently outperform existing solutions.

Our mission is simple yet ambitious: to bring more effective treatments to patients who need them and to make drug development smarter, faster, and more personalized. We collaborate with top hospitals, research institutes, and pharmaceutical companies worldwide. Backed by leading investors, we are a fast-growing, mission-driven startup at the intersection of science and technology.

The role

We are looking for a curious engineering mind to lead the scaling of Orakl’s omics data within the Computational Biology team.

Every organoid grown in our lab is sequenced (WGS and RNA-seq). Our pipelines turn the raw reads into processed molecular profiles, and our models turn those profiles into clinical trial predictions, patient segments and new target candidates. Your mission will be to make omics data available to computational biologists and research engineers in a form they can build on. You will gather their needs and, in coordination with the Data & Infra team, design the data representations, storage layouts and access patterns that make model training and experimentation fast and reproducible. As our biobank expands, this will become a massive omics corpus that must efficiently serve client contracts, strategic partnerships and internal R&D projects.

Starting from processed omics data, you will assess how it is stored and served today, propose solutions to scale it, and drive their implementation, so our scientists and models can rely on it. You will build this hand in hand with our data engineering team, keeping our omics infrastructure consistent with the rest of the company’s stack. You will report to the Head of Computational Biology.

Experience with omics data is a strong plus, not a prerequisite. If you come from the conventional tabular data world and want to master data that is massive, multi-layered and regularly reprocessed, this role is built for you.

What you will do

• Design and build the omics data architecture. Together with our data engineering team, define and evolve how omics data is stored as the corpus grows: what lives in a database, what stays in columnar files on S3, and how it all fits the company’s stack.

• Guarantee reliable ingestion. Make sure every new batch of processed omics data (variant calls, expression, CNV, QC metrics, sample and sequencing tracking) lands reliably and consistently with our data model definition.

• Build and maintain the omics data model. Maintain a curated reference layer as the single source of truth, keep model-ready datasets derived from it separate, and track lineage between the two so every model input can be traced back to its source.

• Make the data easy to use. Build and evolve the access layer so scientists and models get what they need for client contracts, strategic partnerships and internal R&D, without writing joins or asking someone.

What you bring

Must-haves

• 4+ years as a data engineer, with ingestion pipelines you built running in production.

• Master’s in Computer Science, Software Engineering, Data Engineering (or equivalent experience)

• Hands-on experience setting up and maintaining CI/CD for data pipelines and code libraries.

• Strong data modeling experience: you have designed schemas that handle evolving identifiers, versioning and lineage.

• Deep relational database expertise: schema design, partitioning, indexing, query plans, migrations on a live database.

• Deep cloud infrastructure skills: AWS (S3 and core services), infrastructure as code (Terraform or equivalent) and containers (Docker), with sound judgment on what belongs in a database and what stays in files.

• Strong experience in Python and SQL; comfortable with Git, CI and the command line.

• You can propose architecture choices, defend them and converge with engineering peers.

• A real drive to understand omics, including how our data is processed, so you can step in for our Senior Bioinformatician when they are away.

Nice-to-haves

• Omics experience: you have loaded VCF/MAF files or expression matrices. Familiarity with Nextflow / nf-core pipelines.

• Columnar formats and engines (Parquet, Arrow, DuckDB) and an orchestrator (Dagster, Airflow, Prefect).

• Metabase or another BI layer on top of a relational database.

• LIMS or biobank sample-tracking integration.

• A tech-bio background, or prior work next to bioinformaticians.

What we offer

• Mission: work in a cutting-edge environment, on the forefront of cancer research.

• Role: be the engineer behind the data backbone of a distinctive oncology discovery platform — designing, with our data engineering team, how one of the world’s rarest PDO multi-omics and functional datasets is stored, served and scaled, so that scientists and models can build on it every day.

• Impact: take ownership of a critical piece of an early-stage, fast-moving company, with the opportunity to shape it; your engineering decisions will carry through to our science and, ultimately, to patient outcomes.

• Team: join a collaborative, multidisciplinary team where you’ll work alongside stellar clinicians, biologists, and engineers towards a common mission.

Interview Process

1. Hiring Manager Call: Getting to know each other, aligning on expectations and context.

2. Technical Deep Dive: A deep conversation on your experience with production data pipelines, data modeling, and scientific data processing.

3. Technical Case: A system-design and problem-solving exercise representative of the real wet-lab data challenges you'll face at Orakl.

4. Reference Call: A conversation with one or two people you've worked with closely.

5. Founder Interview: A final discussion with our founders on vision, culture fit, and mutual ambitions.

👉 More about Orakl here and here