Specialist - Data Engineering
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Key details
- Location
- São Paulo, Brazil
- Work style
- Hybrid
- Level
- Mid level
- Job type
- Full-time
- Function
- Data & ML
- Focus area
- Data engineering & Analytics
- Posted
- 4w ago
Skills
About the role
Company:
MarshDescription:
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.
What you need to have
- Solid xperience in data engineering (or closely related roles), with demonstrated senior-level ownership.
- Proven experience as the technical owner of at least one production data product (designed, built, deployed, and operated for real stakeholders).
- Strong proficiency in Python and SQL, writing production-grade code.
- Hands-on experience with Databricks.
- Experience building and maintaining CI/CD pipelines.
- Ability to work directly with business stakeholders, lead discussions, clarify ambiguous requirements, and operate proactively with minimal supervision.
- Professional fluency in English.
- Availability for hybrid work (3 days/week in the office).
- Bachelor’s degree in a quantitative field (Computer Science, Engineering, Statistics, Mathematics, or similar) or equivalent practical experience.
What will make you stand out
- Consulting experience.
- Experience in insurance, reinsurance, financial services, or other complex data-rich domains.
- Experience with Azure Data Factory, dbt, or similar orchestration/transformation tools.
- Experience with ARM templates, Databricks Asset Bundles (DABs), or other IaC approaches.
- Experience supporting ML-enabled products in production; familiarity with MLOps concepts.
- Exposure to semi-structured or unstructured data workflows, including text-heavy or AI-enabled use cases.
- Practical experience using AI-assisted development workflows in production.
- Experience with distributed data processing at scale.
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