Databricks Champion (x3)

• Practice Data

• Technologies Business Intelligence Jobs and Data Recruitment

• Location Almere, Netherlands

• Type Contract

About the Role

We are seeking a Databricks Champion to lead the adoption, optimization, and best-practice use of the Databricks platform across our organization. This individual will act as both a technical expert and strategic evangelist, empowering data engineers, analysts, and scientists to deliver scalable, high-performance data solutions.

The ideal candidate will have deep experience in big data architecture, Delta Lake, and Spark, along with a passion for mentoring teams, driving platform excellence, and fostering a data-driven culture.

Key Responsibilities

• Champion Databricks adoption across teams by defining best practices, frameworks, and reusable components.

• Design and implement scalable data pipelines leveraging Apache Spark, Delta Lake, and Databricks SQL.

• Collaborate with cross-functional teams (Data Engineering, Analytics, Machine Learning, and IT) to develop robust, cloud-native data solutions.

• Optimize platform performance, including cluster configuration, cost management, and job orchestration.

• Lead enablement initiatives, including Databricks workshops, office hours, and documentation for engineers and analysts.

• Evaluate and integrate new Databricks features and tools (e.g., Unity Catalog, Delta Live Tables, Model Serving).

• Act as the primary liaison between internal users and Databricks customer success and support teams.

• Promote a data excellence culture by advocating for data quality, governance, and automation across the data lifecycle.

  Qualifications

  Required:

• 5+ years of experience in data engineering, data science, or platform engineering.

• 2+ years of hands-on experience with Databricks (including Delta Lake and Spark optimization).

• Preferred:

• Databricks Certified Data Engineer / Data Scientist.

• Experience implementing Unity Catalog or Delta Live Tables.

• Background in data lakehouse or enterprise-scale analytics platforms.

• Experience with MLflow or machine learning pipelines

• Strong experience with Python, SQL, and PySpark.

• Deep understanding of cloud data architectures (AWS, Azure, or GCP).

• Experience with CI/CD pipelines, infrastructure-as-code, and data governance.

• Strong communication and collaboration skills — able to translate complex concepts for diverse audiences.

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