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# Principal Data Engineer

- **Company:** Optiver
- **Location:** Amsterdam, Netherlands
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Business Analytics Applications, Business Logic, Continuous Integration, Data Transformation, Data Sharing, Data Systems, Metadata Standards, Operational Databases, SQL Databases, Data Classification, Build Management, Pyspark, Data Lineage, Data Pipelines, Databricks
- **Published:** July 15, 2026
- **Apply:** https://www.adzuna.nl/details/5696221168

## About the Role

* 12+ years of experience designing and building production data solutions, with a track record of leading complex technical initiatives end-to-end.
 * Track record of architectural ownership and designing end-to-end data solutions
 * Expert-level SQL and data transformation skills, with strong hands-on experience in dbt, PySpark, or both. You design data products for scale, write tests without friction, and have clear opinions on where each tool's limits are
 * Deep experience with Databricks, Lakehouse architectures, or comparable modern data technologies, including Delta table design, Unity Catalog governance, compute trade-offs, and downstream BI or AI workloads
 * Experience implementing governance at scale, including access control, PII handling, column-level security, data lineage, and data quality management in production environments
 * Experience building trusted business-facing data products within Finance, People/HR, Procurement, Operations, or similar domains. You understand business logic well enough to challenge unclear or incorrect requirements

## Description

* Architect and own delivery of shared data products across Finance, People, Procurement, and Compliance, from design decisions through to production
 * Build and evolve the semantic layer that powers reporting, self-service analytics, conversational analytics, and Databricks Genie, writing the models, tests, and documentation that make it trustworthy
 * Design and build scalable data pipelines, data models, and governed data products using Databricks, dbt, SQL, and PySpark
 * Implement governance capabilities end-to-end, including Unity Catalog access controls, column-level security, data classification, lineage, and data quality standards
 * Drive DataHub adoption by defining metadata standards, lineage definitions, and data ownership models that make discoverability a first-class engineering concern
 * Translate complex and ambiguous requirements from senior stakeholders into production-grade data solutions, owning the problem from conversation to deployed model
 * Set the engineering standard for the team through the quality of your code, architecture decisions, and pull request reviews
 * Partner with the broader data team to continuously improve CI/CD, testing frameworks, observability, and data quality practices


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