{"id":3049016,"url":"https://alion.io/job/marsh-mclennan-specialist-data-engineering","title":"Specialist - Data Engineering","company":{"id":1769686,"name":"Marsh McLennan","domain":"marshmclennan.com","url":"https://alion.io/company/marshmclennan","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":73,"ghost_share":0,"stale_share":0.986,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["São Paulo, Brazil"],"countries":["BR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"closed","first_seen_at":"2026-09-18T00:00:00Z","employer_posted_date":"2026-09-18","last_verified_at":"2026-10-24T01:22:42Z","board_verified":false,"closed_at":"2026-10-24T01:22:42Z","days_open":36,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":36},"description":"Company:\nMarshDescription:\nWe 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.\nExamples 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.\nYou will support pipelines that enable analytics and AI/ML use cases; however, model development is not the focus of this role.\nWhat you can expect\nA fast-paced environment with real business impact and high ownership.\nClose collaboration with product, business, and technology stakeholders across geographies.\nThe opportunity to shape scalable data products and platform capabilities used across the firm.\nA strong engineering culture focused on reliability, observability, and continuous improvement.\nWe count on you to\nBuild and maintain ETL/ELT pipelines, ingestion workflows, and data models on Databricks using Python and SQL.\nTranslate ambiguous business problems into practical data products with clear success criteria.\nOwn solutions end-to-end: design, deployment, monitoring, maintenance, and iterative improvement.\nPartner with product managers, business stakeholders, and technical teams to deliver outcomes.\nApply strong engineering practices, including testing, CI/CD, code review, documentation, lineage, alerting, observability, and production support.\nMake sound trade-offs across performance, reliability, cost, and latency-and communicate them clearly.\nCollaborate with data scientists and analysts on pipelines that support analytics and AI/ML use cases.\nContribute to shared patterns, tooling, and reliability practices across the engineering community.\nUse AI tools pragmatically to accelerate delivery while validating outputs and maintaining accountability.\nWhat you need to have\nSolid xperience in data engineering (or closely related roles), with demonstrated senior-level ownership.\nProven experience as the technical owner of at least one production data product (designed, built, deployed, and operated for real stakeholders).\nStrong proficiency in Python and SQL, writing production-grade code.\nHands-on experience with Databricks.\nExperience building and maintaining CI/CD pipelines.\nAbility to work directly with business stakeholders, lead discussions, clarify ambiguous requirements, and operate proactively with minimal supervision.\nProfessional fluency in English.\nAvailability for hybrid work (3 days/week in the office).\nBachelor’s degree in a quantitative field (Computer Science, Engineering, Statistics, Mathematics, or similar) or equivalent practical experience.\nWhat will make you stand out\nConsulting experience.\nExperience in insurance, reinsurance, financial services, or other complex data-rich domains.\nExperience with Azure Data Factory, dbt, or similar orchestration/transformation tools.\nExperience with ARM templates, Databricks Asset Bundles (DABs), or other IaC approaches.\nExperience supporting ML-enabled products in production; familiarity with MLOps concepts.\nExposure to semi-structured or unstructured data workflows, including text-heavy or AI-enabled use cases.\nPractical experience using AI-assisted development workflows in production.\nExperience with distributed data processing at scale.\nMarsh (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. 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