Director – Data Engineering

Marsh McLennan

. Lead a delivery squad of 4–8 data engineers, including employees and contractors .

Posted 10/14/2026full-timeMumbai • IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading data engineering teams, with a strong focus on SQL, Python, and Spark for building and maintaining data pipelines. Proficient in cloud data platforms, particularly Databricks, and committed to ensuring data quality and operational excellence.

Highest-signal resume keywords

Data Engineering LeadershipSQL ProficiencyPython ProficiencyDatabricks ExperienceCI/CD Implementation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonSparkData ModelingData Quality ChecksInfrastructure-as-CodeAutomated TestingData GovernanceDocument Ingestion PipelinesDbt

Soft Skills

Clear CommunicationMentoringTeam Cohesion

Tools & Technologies

DatabricksUnity CatalogDelta LakeAWSAzureGitDataOpsPipeline Orchestration

Industry Keywords

Financial ServicesInsuranceBenefitsConsultingMedallion Architecture

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonSparkSQLUnity

About the role

Key responsibilities & impact

Lead a delivery squad of 4–8 data engineers, including employees and contractors
Set technical direction, decompose work, manage sprint-level execution, and remain accountable for squad deliverables
Write code, build pipelines, and debug production issues using SQL, Python, and Spark
Design data models, medallion architectures, ingestion contracts, and reusable patterns
Ensure schema evolution, data quality checks, idempotency, and lineage
Work directly with Engagement Leads and business stakeholders to translate business problems into data solutions
Establish engineering standards including CI/CD, version control, testing, code review, and documentation
Operate Databricks with Unity Catalog, Delta Lake, Spark, and medallion architecture on AWS and Azure
Use infrastructure-as-code, Git-based workflows, and pipeline orchestration tooling
Mentor engineers, set expectations, manage performance, build team cohesion, and improve developer experience and operational excellence
Deliver production pipelines and curated datasets for business users
Build reusable assets, documented schemas, tested pipelines, and onboarding patterns
Improve code quality, testing coverage, and operational reliability across the squad

Requirements

What you’ll need

5+ years of hands-on data engineering experience, with at least some time leading or mentoring other engineers
Strong proficiency in SQL, Python, and Spark, used recently and regularly
Real experience with cloud data platforms (Databricks strongly preferred; Snowflake, Synapse, or similar accepted) on AWS, Azure, or GCP
Demonstrated ability to lead a mixed team of employees and contractors
Fluency with Git, CI/CD, automated testing, infrastructure-as-code, and DataOps
Intellectual curiosity about the business domain
Clear communication skills
Experience with Databricks specifically: Unity Catalog, Delta Lake, medallion architecture, and Databricks workflows
Experience with dbt or similar transformation frameworks
Familiarity with data governance patterns: lineage, access controls, metadata management, and data quality frameworks
Prior work in financial services, insurance, benefits, or consulting environments
Experience with document or unstructured data ingestion pipelines
Exposure to LLM-based tooling, AI-assisted development, or analytics engineering

Benefits

Comp & perks

Professional development opportunities
Supportive leaders
Inclusive and flexible work environment
Career opportunities
Benefits and rewards to enhance well-being
Flexibility to work remotely
Collaboration, connections, and professional development benefits from working in the office