Location
Hybrid (São Paulo, Brazil)
Employment
Full-Time
Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Jul 22, 2026. Marsh McLennan scores B on the Alion truth index.
Overview
Company
Impact
Profile match
Marsh McLennan is a global professional services firm in risk, strategy and people, headquartered in New York and listed on the New York Stock Exchange. It operates through four businesses, Marsh in insurance broking, Guy Carpenter in reinsurance, Mercer in health, wealth and benefits consulting and Oliver Wyman in management consulting, with about 90,000 colleagues in some 130 countries. It hires catastrophe risk analysts, reinsurance brokers, benefits consultants, sales executives, paralegals, software engineers and IT support staff in hubs such as London, Cluj-Napoca and Mexico City.
Company:
Guy CarpenterDescription:
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.
- Hands-on experience with Databricks (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; proactive, low-supervision working style.
- Professional fluency in English.
- Availability for hybrid work in Sao Paulo
- Bachelor's degree in a quantitative field (or equivalent practical experience).
What makes 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 frameworks.
- Experience building or supporting APIs, integrations, async workflows, or queue-based systems.
- 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.
- Familiarity with BI tools such as Power BI, Tableau, or Looker.
If you meet many, but not all, of the qualifications above, we still encourage you to apply.
At Guy Carpenter, a Marsh business, you can be your best. We work on challenges that matter with colleagues who help bring out our best. Our uniquely collaborative environment will empower you to focus on your personal and professional success, learning from top specialists in the (re)insurance industry and leading you towards a rewarding and impactful career.Guy Carpenter is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information about Guy Carpenter, visit guycarp.com, or follow us on LinkedIn and X.Marsh is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.Free account
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