Data Engineering Specialist

What can you expect?

• Join the Data Strategy team to build and operate firm-wide data products and bespoke business applications for a global reinsurance business.
• A customer-facing, product-oriented role focused on turning business problems into reliable, stakeholder-facing outcomes end to end.
• Hands-on work building curated datasets (bronze-to-gold), spatial/geo solutions, and pipelines combining client, broking, and actuarial data.
• Work closely with global colleagues (US/Europe) from the Sao Paulo hub; professional fluency in English is required.
• Hybrid model in Sao Paulo:
• Support analytics and AI/ML-enabled use cases through robust pipelines (model development and core MLOps are not the primary focus).
• Opportunity to own production-grade data products from design through deployment, monitoring, and continuous improvement.
• High-impact work on data products used daily by analysts and AI agents across the business.
• Exposure to complex, data-rich domains (reinsurance) and cross-functional collaboration with product, business, and technical stakeholders.
• A strong engineering culture emphasizing quality, observability, and reliable delivery.

We will 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 improvement.
• Work directly with product managers, business stakeholders, and technical partners.
• Apply strong engineering practices: testing, CI/CD, code review, observability, documentation, lineage, alerting, and support.
• Make sound trade-offs across performance, reliability, cost, and latency and communicate them clearly.
• Partner with data scientists and analysts on pipelines that support analytics and AI/ML use cases.
• Contribute to shared engineering patterns, tooling, and reliability practices.
• Use AI tools pragmatically to improve delivery while validating outputs.

What you need to have:

• Experience in data engineering (or closely related roles), with evidence of operating at a senior level.
• Proven technical ownership of at least one production data product used by real stakeholders.
• Strong proficiency in Python and SQL; ability to write production-grade code.