Data Engineering Specialist

We are seeking a Senior Data Engineer to join a team supporting and extending a Databricks data platform and building firm-wide data products for a global reinsurance business.

Examples of what you may build include: IaC and CI/CD to support deployment of Databricks Apps and Genie Spaces; curated bronze-to-gold datasets used daily by analysts and AI agents; geo/spatial data solutions for catastrophe modeling; pipelines combining client, broking, and actuarial data to support placements, renewals, and advisory work; ingestion from vendor APIs and internal data platforms; and structured/unstructured data catalogs that enable AI agents to answer business questions accurately using production data.

You will support pipelines that enable analytics and AI/ML use cases; however, model development is not the focus of this role.

What you can expect

• A fast-paced environment with real business impact and high ownership.
• Close collaboration with product, business, and technology stakeholders across geographies.
• The opportunity to shape scalable data products and platform capabilities used across the firm.
• A strong engineering culture focused on reliability, observability, and continuous improvement.

We count on you to

• Build and maintain ETL/ELT pipelines, ingestion workflows, and data models on Databricks using Python and SQL.
• Translate ambiguous business problems into practical data products with clear success criteria.
• Own solutions end-to-end: design, deployment, monitoring, maintenance, and iterative improvement.
• Partner with product managers, business stakeholders, and technical teams to deliver outcomes.
• Apply strong engineering practices, including testing, CI/CD, code review, documentation, lineage, alerting, observability, and production support.
• Make sound trade-offs across performance, reliability, cost, and latency—and communicate them clearly.
• Collaborate with data scientists and analysts on pipelines that support analytics and AI/ML use cases.
• Contribute to shared patterns, tooling, and reliability practices across the engineering community.
• Use AI tools pragmatically to accelerate delivery while validating outputs and maintaining accountability.