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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Lead - Data GCP & Databricks - **Company:** Gapstars - **Location:** ALMERE, Netherlands - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Automation of Tests, BigQuery, Code Review, Continuous Integration, Data Infrastructure, Data Transformation, Data Flow Control, Python (Programming Language), Machine Learning, Standard Sql, SQL Databases, Data Streaming, Systems Integration, Management of Software Versions, Google Cloud, SAP Integration Solutions, Apache Spark, Git, Data Layers, Data Lakes, Data Lineage, Machine Learning Operations, Terraform, Data Pipelines, Docker, Databricks - **Published:** September 1, 2026 - **Apply:** https://gapstars.net/tech/career-listings/job-1621/ ## About the Role * Strong hands-on Google Cloud Platform (GCP) experience is mandatory. * Strong experience with BigQuery and GCP-based data pipelines. * Nice-to-Have * Experience with: + Delta Lake + Unity Catalog + Databricks Workflows + MLflow + Apache Spark * GCP services such as: + Dataflow + Cloud Run + Composer / Airflow + Google Cloud Storage * Terraform and Docker experience. * Experience with SAP integrations. * Semantic modeling experience. * AI-enabled analytics experience. * Retail or e-commerce experience. * * Hands-on experience with Databricks in production environments. * Strong SQL and Python skills. * Experience building and operating modern data platforms or lakehouse environments. * Experience with dbt or similar transformation frameworks. * Strong understanding of data modeling and semantic layers. * Experience supporting ML workflows and Data Science teams. * Experience with data quality, governance, security, and monitoring. * Experience with CI/CD and Git. * Strong ownership and stakeholder communication. ## Description As a Senior Data Engineer, you will work across Google Cloud Platform (GCP) and Databricks, building and evolving scalable data pipelines, data products, and analytics-ready datasets. The current environment is approximately 60-70% GCP-focused, so strong hands-on GCP experience is mandatory. Initially, your work will primarily focus on the existing GCP platform, with increasing ownership of Databricks pipelines, integrations, and lakehouse capabilities as the platform evolves. You will also work closely with Data Scientists and Analytics teams to deliver trusted, governed, AI-ready and reporting-ready datasets., 1) GCP Data Engineering * Design and maintain scalable data architectures and pipelines on GCP. * Build and optimize solutions using BigQuery, Dataflow, Cloud Run, Composer, and GCS. * Develop reliable pipelines supporting analytics, reporting, and machine learning workloads. * Translate business requirements into scalable technical solutions. * Maintain high standards around performance, reliability, security, and cost. 2) Databricks Engineering * Own and develop Databricks pipelines and integrations. * Work with Delta Lake, Unity Catalog, Workflows, MLflow, and Spark. * Support the gradual expansion of Databricks within the wider data platform. * Ensure GCP and Databricks workloads follow consistent engineering, governance, and security standards. * Help shape future lakehouse architecture and integration patterns. 3) Data Transformation & Semantic Layer * Develop transformation workflows using SQL, Python, and dbt. * Build and maintain reusable semantic layers and data models. * Deliver datasets that are ready for analytics, reporting, AI, and ML use cases. * Establish standards for testing, documentation, versioning, and deployment. 4) Data Quality, Governance & Reliability * Implement data quality controls and automated testing. * Ensure data accuracy, governance, security, and accessibility. * Monitor pipeline health, freshness, performance, and operational stability. * Troubleshoot incidents and drive continuous improvement. * Support data lineage, access controls, and lifecycle management. 5) AI / ML Enablement * Work closely with Data Scientists and Analytics teams. * Understand ML workflows and the data requirements behind them. * Build trusted and reusable datasets for ML and AI use cases. * Support ML pipelines and integrations, including the use of MLflow where applicable. * Help create AI-ready datasets through the semantic layer. 6) Platform Ownership & Engineering Standards * Contribute to infrastructure and deployment standards using Terraform, Docker, CI/CD, and Git. * Promote engineering best practices around testing, documentation, code reviews, and operational ownership. * Provide technical guidance and mentor other engineers. * Document architectures, data flows, integrations, and operational processes. 7) Collaboration & Stakeholder Management * Work with Product Owners, Data Scientists, Analysts, Engineers, and business stakeholders. * Translate business needs into scalable technical solutions. * Communicate technical decisions, trade-offs, risks, and progress clearly. * Take ownership of solutions from design through production., Technical Lead - Data GCP & Databricks ## 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) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [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 - [Got AI ideas but no money? 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