You’ll join a small, senior team where your work will have immediate impact, and you’ll have ownership over the systems you build.

You’ll work on technically challenging problems such as:

• Building dataset lineage for rapidly evolving generative models
• Tracking model-family clusters across synthetic media types
• Designing reproducible forensic benchmarks at scale
• Managing large-scale image/video datasets with auditable provenance
• Creating deterministic dataset builds for research and production environments

What You’ll Drive

• Data platform architecture: Define unified schemas, lineage, and dataset versioning for large image/video + context data.
• Ingestion at scale: Build reliable pipelines from research repos, APIs, and internal generators; automate connectors and jobs.
• Quality & governance: Implement deduplication, validation, health dashboards, and drift/coverage checks with auditable lineage.
• Curation & access: Deliver one-command dataset builds, deterministic splits, and fast sampling tools for training/eval.
• Performance & cost: Tune S3/object storage layouts, partitioning, and lifecycle policies for speed and spend.
• Orchestration & ops: Productionize pipelines with CI/CD, containerization, scheduling/monitoring, and safe rollbacks.
• Reliability & operations: Build for simplicity and observability; participate in a planned, compensated support rotation.
• Engineering productivity: Create internal tools/CLIs, docs, and templates that make everyone faster.

Must haves

• Strong software engineering foundation: Master’s in Computer Science, Data Engineering, or a related field.
• Production experience: 5–8+ years building and operating data platforms for large unstructured datasets (images/video).
• Data lifecycle ownership: Ingest → validate → catalog → version → sample/serve → monitor.
• Pipelines & orchestration: Experience with modern schedulers (e.g., Airflow/Prefect) and containerized jobs.
• Storage & formats: Hands-on with object storage (e.g., S3), columnar formats/partitioning, and performance tuning.
• Versioning & lineage: Experience with dataset versioning and reproducibility (e.g., DVC/lakeFS/Delta or equivalents).
• Quality at scale: Deduplication, schema/label checks, and automated QC gates in CI.
• Security & privacy: IAM, access controls, and privacy-aware workflows suitable for regulated customers.
• Domain awareness: Familiarity with digital forensics, misinformation threats, or synthetic media — and willingness to deepen expertise.
• Flexibility: Comfortable moving between data engineering, infra, and tooling tasks when needed.
• Mindset & delivery: Thrive in a fast-moving environment; proactive problem-solver; ship, measure, simplify.
• Communication: Excellent written and verbal skills; explain complex ideas clearly.
• Independence: Deliver quality work on time without constant oversight.
• Language: Fluent in English.

Nice-to-haves

• Streaming & events: Kafka/Kinesis or similar for near-real-time ingestion.
• Vector search: Experience with embedding stores or similarity search at scale.
• Synthetic data: Building pipelines to generate/stress-test rare scenarios.
• Cloud & on-prem: Terraform/CDK, Kubernetes, and hybrid/on-prem data deployments.
• FinOps: Cost monitoring and optimization for data workloads.
• Technical track record: Strong GitHub, open-source contributions, publications, patents, or public talks.
• Leadership: Mentoring and guiding technical direction.
• Dutch language: Fluency is a plus.

Key Deliverables (First 90 Days)

• A unified schema + catalog with key datasets onboarded, versioned, and reproducibly built via one command.
• Automated QC gates (dedup/validation) with a red/amber/green dataset health dashboard and clear lineage.
• Fast sampling/curation tools for the ML team, plus cost controls (storage layouts, lifecycle policies) in place.
• Data migration: Inventory and migrate existing/legacy datasets into the new platform; reformat to the new schema, backfill metadata, validate checksums/lineage, and deprecate legacy paths with a rollback plan.

Compensation & benefits

• Own the backbone: Define schemas, lineage, and dataset versioning used across research and production.
• Company participation: Meaningful equity/virtual shares aligned with company growth.
• Flexible work: Hybrid (Delft), flexible hours, minimal ceremony, async-first collaboration.
• Data platform mandate: Real say in stack choices (orchestration, catalog, storage/layout) and time to implement them right.
• Repro & auditability: Space to enforce deterministic builds, splits, and traceable lineage—no heroics needed.
• Quality culture: Backing to implement dedup, drift/coverage checks, and dataset health dashboards org-wide.
• FinOps mindset: Budget and support to balance speed, reliability, and total cost.
• Pragmatic on-call: Planned, compensated rotation with automation-first recovery and rollback plans.
• Growth path: IC track to Staff/Principal; opportunities to mentor and codify data standards.
• Learning budget: Annual budget for courses/books + two data/ML-infra conferences per year.
• Home office: Modest stipend for an ergonomic setup; commuting support (public transport or mileage).
• Relocation + visa: Visa sponsorship and relocation support for internationals.

Join us and be part of a company committed to creating a more secure and trustworthy digital future. Apply today to become part of our mission-driven team!