Specialist - Data Engineering

Marsh; São Paulo; Híbrido; BRL 180 000 - 340 000; Tempo integral

Resumo da oferta

Marsh seeks 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.

You will build and maintain ETL/ELT pipelines, curating datasets used daily by analysts and AI agents, while ensuring reliability, observability, and scalable data solutions across geographies.

Qualificações

• Senior data engineering experience with ownership of production data products.
• Proficiency in Python and SQL for production-grade code.
• Hands-on Databricks experience and CI/CD pipelines.
• Fluent English and ability to work with global stakeholders.

Responsabilidades

• Build and maintain ETL/ELT pipelines and data models on Databricks.
• Translate business problems into practical data products with clear success criteria.
• Own end-to-end solutions: design, deployment, monitoring and iteration.
• Collaborate with product, business, and tech teams to deliver outcomes.
• Apply strong engineering practices: testing, code reviews, documentation, observability.

Conhecimentos

Python

SQL

Databricks

CI/CD

English

Formação académica

Bachelor's degree

Ferramentas

dbt

Azure Data Factory

Databricks Asset Bundles (DABs)

ARM templates

Descrição da oferta de emprego

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

• Solidxperience 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.

Marsh (NYSE: MRSH) is 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, visit marsh.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.