> Markdown version of [/jobs/ext/3031663-principal-data-engineer](https://www.wearedevelopers.com/jobs/ext/3031663-principal-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# 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, Data Layers, Build Management, Pyspark, Data Lineage, Data Management, Data Pipelines, Databricks
- **Published:** September 23, 2026
- **Apply:** https://www.nationalevacaturebank.nl/vacature/a45888c5-63ba-44a5-867c-2874de7d78ab/principal-data-engineer

## 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

Every major business function, including Finance, People, Procurement, Tax, and Compliance, operates on its own version of the truth: fragmented, siloed, and manually managed. You'll build the governed data foundation that replaces this, greenfield, on a modern stack, with real architectural ownership and production delivery in your hands.
The Problem You're Solving
One truth across multiple domains. Multiple business functions. Multiple definitions of the same metrics. Your role is designing and building the unified semantic layer that brings this together. This is a real modelling challenge where getting the business logic right matters as much as getting the code right.
Governance that's engineered, not bolted on. Lineage, access control, column masking, PII classification, and data stewardship are designed into every domain from day one using Unity Catalog and DataHub. The goal is a governance model rigorous enough to hold up to regulatory scrutiny and trusted enough that business teams stop building their own extracts.
The data layer that AI depends on. Optiver is building conversational analytics and AI agents that query business data through a semantic layer. What Databricks surfaces to the business depends entirely on what you build underneath it.
What you'll do

 + 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 , 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
Who you are, + Training opportunities, discounts on health insurance, and fully paid first-class commuting expenses.

 + Extensive office perks, including breakfast, lunch and dinner, world-class barista coffee, in-house physio and chair massages, organized sports and leisure activities, and Friday afternoon drinks.
 + Training and continuous learning opportunities, including access to conferences and tech events.
Please note:

 + We do not require any assistance from third-parties including agencies in the recruitment of this role
 + We cannot accept applications via email
diversity and inclusion.
Footer navigation
At Optiver, we continuously quote buy and sell prices across financial markets, using our own capital and advanced technology to provide liquidity at scale across products, venues and market conditions.


## Related Videos

- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [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)
- [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source)
- [Why Your AI Agent Keeps Hallucinating Your Data: Building Deterministic Context Layers](https://www.wearedevelopers.com/videos/2055-why-your-ai-agent-keeps-hallucinating-your-data-building-deterministic-context-layers)
- [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)
- [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)
- [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)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)