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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Head of Data - **Company:** TBAuctions - **Location:** Amsterdam, Netherlands - **Experience:** Expert - **Salary:** €7,500.0 - €9,167.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Apache HTTP Server, Microsoft Azure, Software as a Service, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Masking, DevOps, Dimensional Modeling, Apache Hive, Machine Learning, Meta-Data Management, Metadata Repositories, Open Source Technology, Prometheus, Grafana, Apache Spark, Git, Data Lakes, Pyspark, Debezium, Kubernetes, Apache Kafka, Data Management, Terraform, Databricks - **Published:** July 18, 2026 - **Apply:** https://nl.indeed.com/viewjob?jk=291e7f642010b105 ## About the Role 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) ## 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)