Principal Data Engineer

About the role

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 Genie surfaces to the business depends entirely on what you build underneath it.

What you'll do

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Architect and own delivery of shared data products across Finance, People, Procurement, and Compliance, from design decisions through to production

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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

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Design and build scalable data pipelines, data models, and governed data products using Databricks, dbt, SQL, and PySpark

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Implement governance capabilities end-to-end, including Unity Catalog access controls, column-level security, data classification, lineage, and data quality standards

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Drive DataHub adoption by defining metadata standards, lineage definitions, and data ownership models that make discoverability a first-class engineering concern

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Translate complex and ambiguous requirements from senior stakeholders into production-grade data solutions, owning the problem from conversation to deployed model

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Set the engineering standard for the team through the quality of your code, architecture decisions, and pull request reviews

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Partner with the broader data team to continuously improve CI/CD, testing frameworks, observability, and data quality practices

Who you are

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12+ years of experience designing and building production data solutions, with a track record of leading complex technical initiatives end-to-end.

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Track record of architectural ownership and designing end-to-end data solutions