4 months ago
São Paulo, BrazilSenior
Responsibilities
- Build and maintain ETL/ELT pipelines, ingestion workflows, and data models on Databricks using Python and SQL.
- Build curated bronze-to-gold datasets, spatial/geo solutions, and pipelines combining client, broking, and actuarial data.
- Own production data products from design through deployment, monitoring, maintenance, and continuous improvement.
- Translate ambiguous business problems into practical data products with clear success criteria.
- Collaborate directly with product managers, business stakeholders, technical partners, data scientists, and analysts.
- Apply engineering practices including testing, code review, observability, documentation, lineage, alerting, and production support.
- Make trade-offs across performance, reliability, cost, and latency and communicate them clearly.
- Contribute to shared engineering patterns, tooling, and reliability practices.
- Use AI tools pragmatically to improve delivery while validating their outputs.
Requirements
- Experience in data engineering or a closely related role, 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, including production-grade coding.
- Hands-on experience with Databricks is mandatory.
- Experience building ETL/ELT pipelines and production data systems.
- Experience with data quality, integrity checks, and operational reliability in production.
- Ability to work directly with business stakeholders and turn ambiguous requirements into practical solutions with limited supervision.
- Professional fluency in English.
- Availability for hybrid work in São Paulo.
- Bachelor's degree in a quantitative field or equivalent practical experience.
- Consulting experience is preferred.
- Experience in insurance, reinsurance, financial services, or other complex data-rich domains is preferred.
- Experience with Azure Data Factory, dbt, or similar orchestration/transformation frameworks is preferred.
- Experience with APIs, integrations, asynchronous workflows, or queue-based systems is preferred.
- Experience supporting ML-enabled products in production and familiarity with MLOps concepts are preferred.
- Exposure to semi-structured or unstructured data workflows, including text-heavy or AI-enabled use cases, is preferred.
- Practical experience with AI-assisted development workflows in production is preferred.
- Experience with distributed data processing at scale is preferred.
- Familiarity with Power BI, Tableau, or Looker is preferred.
Benefits
- Hybrid work in São Paulo with colleagues expected to work in the local office or onsite with clients at least three days per week.
- Office-based teams designate at least one weekly anchor day for the full team to work together in person.
- The company describes a diverse, inclusive, flexible work environment with collaboration, connection, and professional development benefits.
About Marsh
Marsh is a global insurance broker and risk advisor that helps businesses, public entities, and individuals assess, transfer, and manage risk through insurance placement, captives, and advisory services. It sells brokerage, risk consulting, and claims management across industries, including cyber and property, and operates in more than 130 countries. Marsh is a business of Marsh McLennan and is headquartered in New York.