Head of Data
Role details
Job location
Tech stack
Job description
As Head of Data, you own the company's data platform: making sure data is ingested, processed, governed and made available reliably and securely for analytics, AI, machine learning and operational applications. You lead the Data Platform Engineering team and are accountable for both the team's growth and the platform's operational excellence.
We're invested in Databricks and Azure today, but we're looking for someone who thinks beyond the current stack - in terms of architecture, engineering practices, open source opportunities and long-term capability building. You report to the CFO.
What you will do
- Own the technical direction of the data platform, including its infrastructure and budget
- Develop a roadmap that balances business priorities with long-term platform investments, and make the architectural calls that keep it scalable, secure and maintainable
- Lead ingestion and integration of data from internal platforms, SaaS applications, ERP/CRM systems, APIs and third-party sources, and build and operate reliable batch and streaming pipelines
- Develop and maintain our Medallion (bronze/silver/gold) architecture, and evolve the gold layer to better serve day-to-day data and BI needs
- Implement platform-wide governance - metadata management, cataloguing, lineage and access controls - in partnership with Security and Architecture to keep us GDPR and security compliant
- Enable ML & AI workloads through scalable pipelines and high-quality datasets, and build self-service capabilities for analysts and power users
- Lead and develop the Data Platform Engineering team, setting engineering standards, coding practices and delivery priorities
What you bring
You think like an architect and work like an engineer: you can set a long-term technical direction and still get into the details of a Spark job or a pipeline that's misbehaving. You've led engineering teams before, and you know how to turn a roadmap into shipped, reliable infrastructure. You partner naturally with Product, Engineering, Machine Learning, Architecture, Security and business stakeholders, and you bring the same pragmatism to picking battles as you do to picking technology.
Requirements
8+ years in Data Engineering or Data Platform roles, including 3+ years leading engineering teams, with experience operating enterprise-scale data platforms in production
- Strong knowledge of Apache Spark internals (shuffling, driver vs. executor architecture, lazy evaluation) and experience building reliable, production-grade ELT/ETL pipelines
- Experience designing architectures that combine batch and streaming processing (e.g. Lambda or Kappa), including checkpointing, state management, exactly-once semantics and watermarking
- Experience with CDC technologies such as Debezium
- Strong experience with Databricks or a comparable lakehouse platform, and with data modeling frameworks such as Kimball dimensional modeling
- Experience implementing Medallion Architecture using a transformation tool such as dbt
- Experience with cloud platforms (Azure preferred; AWS or GCP acceptable) and Infrastructure as Code (e.g. Terraform) and CI/CD pipelines
- Experience with data catalogs, data masking techniques and data governance platforms (DataHub, Atlan, OpenMetadata)
Technical skills
- Databricks, Apache Spark (Spark SQL, PySpark), Apache Kafka, Apache Airflow
- Table formats: Delta Lake, Apache Iceberg
- Kubernetes - deploying and maintaining stateful services (e.g. OpenMetadata)
- Prometheus & Grafana
- Git and DevOps practices (CI/CD)
Benefits & conditions
- Yearly salary €90,000 - €110,000
- Good pension scheme
- Bonus scheme (up to 1 gross monthly salary per year)
- 25 vacation days
- Laptop and iPhone
- Training opportunities
- Working in a team of professionals, where hard work is well combined with humor