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Marsh McLennan
Marsh McLennan
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Marsh McLennan is a global professional services firm working across risk, strategy, and people-related advisory services. Its businesses include Marsh, which provides insurance brokerage and risk management; Guy Carpenter, focused on reinsurance and capital strategies; Mercer, serving clients in health, wealth, and career consulting; and Oliver Wyman, specializing in strategy, economics, and brand consulting. With a workforce of more than 85,000 employees worldwide, Marsh McLennan brings together expertise across finance, consulting, marketing, and related professional services. The company’s hiring landscape spans roles supporting client advisory, risk analysis, business strategy, consulting, and operational teams across its four businesses.
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Manager, Data Engineering - Pune (Hybrid)

Lead data engineering initiatives for Marsh McLennan’s global risk and consulting business. Build scalable Databricks, PySpark, SQL, Delta Lake, and Lakehouse solutions for governed analytics.

INIndiaHybridFull-timeMiddleSeniorEnglish RequiredData Engineer
AWSCloudInformaticaPySparkSparkSQL

Description

  • Design and deliver scalable pipelines on the Databricks Lakehouse platform
  • Create and maintain data products based on Medallion Architecture
  • Develop pipelines for data ingestion, transformation, and consumption
  • Build business-ready Gold-layer datasets for reporting and analytics
  • Manage Delta Lake tables and related data assets
  • Implement incremental processing with Merge, Upsert, and CDC patterns
  • Improve Delta table performance through partitioning, compaction, and tuning
  • Assess legacy data flows and SQL, then convert them into efficient PySpark pipelines
  • Record transformation rules and migration strategies
  • Develop scalable PySpark applications in Databricks
  • Create reusable engineering frameworks and components
  • Tune Spark workloads and review execution plans
  • Establish data quality checks, reconciliation procedures, and validation frameworks
  • Write complex SQL, stored procedures, and efficient transformation logic
  • Use SQL for data analysis, profiling, and issue resolution
  • Improve query performance and data models for large-scale datasets
  • Support lineage, metadata, governance, CI/CD, and deployment automation
  • Partner with data architects, business stakeholders, analysts, and platform teams

Requirements

  • Demonstrated hands-on expertise with Databricks and Spark-based data engineering
  • Experience applying Medallion Architecture across Bronze, Silver, and Gold layers
  • Background designing and operating Delta Lake solutions
  • Solid understanding of Lakehouse Architecture concepts
  • Experience building scalable pipelines for enterprise analytics platforms
  • Knowledge of data quality, lineage, governance, and observability practices
  • Experience with Informatica is an advantage
  • Cloud engineering experience, preferably with AWS
  • Practical expertise in Databricks, PySpark, SQL, Delta Lake, Medallion Architecture, data modeling, and distributed processing at scale

Benefits

  • Opportunities for professional growth and development
  • Engaging work with supportive leadership
  • Inclusive and energetic workplace culture
  • Flexible working environment
  • Multiple career development paths
  • Benefits and rewards focused on employee well-being
  • Option to work remotely
  • Office-based collaboration, relationship building, and professional development opportunities

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