Data Tech Data Lead Hoofddorp · hybrid

Devleaps; Hoofddorp; Hybrid; EUR 120,000 - 180,000; Full time

Job summary

Devleaps is building data platforms that move, transform and serve data reliably for finance, ops and product teams. We seek a Data Tech Lead who sets architecture, quality and governance and leaves a team capable of running the platform.

You will own the data estate architecture, establish contracts and schema evolution, and guide data engineers and analytics engineers through design reviews and hands-on collaboration.

Qualifications

• 10+ years of data or software engineering with lead/architect experience.
• Proven design and delivery of data platforms (warehouses, lakehouses, streaming) with governance.
• Fluent Dutch and professional English.

Responsibilities

• Lead technical direction on data engagements and architecture.
• Embed as a technical anchor in client teams; coach engineers and analytics engineers.
• Drive modernisation from legacy warehouses to governed lakehouses.

Skills

Dutch

English

Data architecture

Leadership

Tools

dbt

Airflow

Dagster

Databricks

Spark

Delta Lake

Iceberg

Kafka

Job description

Engineering is human. So are you. Nobody notices the pipes when they work.

At Devleaps, we build data platforms that move, transform and serve data reliably, so finance, ops and product decide from the same numbers. We're looking for a Data Tech Lead who sets the architecture for that platform, holds the line on quality and governance, and leaves a team able to run it.

WHAT YOU'LL DO

• Lead technical direction on data engagements: own the architecture of the data estate — ingestion, layering, modelling, serving — and set the engineering standards it is built to.
• Set the modelling and contract discipline: dimensional and lakehouse layering, clear raw-to-curated separation, data contracts with downstream consumers, and a real schema evolution strategy.
• Embed as a technical anchor in client teams: guide data engineers and analytics engineers through design, and raise the bar through review, pairing, and structured feedback.
• Make quality and lineage architectural, not optional: quality gates between stages, freshness and volume SLOs, lineage that answers 'where did this number come from', and alerting that halts downstream dependencies.
• Drive modernisation at scale: lead migrations from legacy warehouses, hand-rolled ETL and fragmented estates towards a governed lakehouse — with a clear plan, not just a vision.
• Own the technical narrative: facilitate architecture reviews and workshops, and translate data trade-offs — cost, latency, granularity, governance — into decisions a CTO or CDO can sign off on.
• Remove what slows teams down: find the systemic bottleneck, whether it is tooling, ownership, or a model nobody can extend safely, and fix the actual constraint.

YOU ARE

• A seasoned engineer who has led: you've made architectural calls, mentored engineers, and owned outcomes — not just delivered tickets.
• A consultant at heart: you ask before you prescribe, push back with evidence, and know the best solution isn't always the first one you think of.
• Rigorous about correctness: you would rather delay a dashboard than let a subtly wrong number reach a board pack, and you know how easily that happens.
• A multiplier: you measure success by how much better the team and the platform get, not just by what you personally shipped.
• Human: you have opinions, sharp questions, hobbies. That's fine — we're a little bit geeky and proud of it.

YOU'LL BRING

• 6–10+ years of data or software engineering experience, with at least a few years in a lead, principal or data architect capacity.
• A track record of designing and delivering data platforms in production — warehouses, lakehouses, and streaming pipelines with SLOs, lineage and governance.
• Deep data modelling expertise: dimensional (Kimball), data vault or lakehouse layering, and a defensible view of when each is the right call.
• Strong SQL plus solid Python (or Scala) for the transformations and connectors a warehouse cannot express.
• Hands-on experience with a modern transformation and orchestration stack: dbt, Airflow, Dagster, Databricks, or similar — you have built and maintained these, not just used them.
• Working knowledge of distributed processing (Spark) and open table formats (Delta Lake, Iceberg) and their trade-offs.
• Experience with streaming and CDC ingestion (Kafka, Azure Event Hubs, Debezium), and a clear view of when streaming earns its complexity.
• Hands-on experience with at least one major cloud data platform — Azure (Fabric, Synapse), AWS, or GCP (BigQuery). We see all three in data engagements.
• Practical grasp of data governance: cataloguing, access control, retention, and GDPR-relevant handling of personal data.
• Proven consultancy skills: stakeholder management, difficult conversations, and clear recommendations under pressure.
• Fluent Dutch, professional English.