Senior Data Engineer

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

• Design, build, and optimise data pipelines, models, and data products using Databricks, dbt, SQL, and PySpark, transforming fragmented business data into trusted and reusable assets

• Build and maintain semantic models that power reporting, self-service analytics, conversational analytics, and Databricks Genie

• Implement governance capabilities using Unity Catalog, including lineage, access controls, data classification, and data quality standards across every domain you deliver

• Contribute to DataHub adoption through metadata management, lineage capture, and data ownership standards that improve discoverability and trust

• Partner with business stakeholders to understand requirements and translate them into production-grade data solutions

• Drive engineering best practices through testing, CI/CD, documentation, code reviews, and observability

• Collaborate with technical leads to continuously improve data architecture, engineering standards, and delivery practices

Who You Are

• 8+ years of experience designing and building production data pipelines, data models, and analytical data solutions

• Advanced SQL skills with strong hands-on experience using dbt, PySpark, or both. You build scalable data solutions, write tests as standard practice, and have strong data modelling judgement

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