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Senior Principal Engineer – Data Engineering
Marsh McLennanSenior Data Engineering Analyst at Marsh designing scalable data infrastructure across multi-cloud environments. Collaborating with analytics and cloud architects to leverage data and analytics solutions.
Tech Stack
Tools & technologiesApacheAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonScalaSparkSQL
About the role
Key responsibilities & impact- Design, develop, and maintain scalable data pipelines and architectures across Azure, AWS, and/or GCP using tools like Azure Data Lake Storage, Azure Data Factory, Databricks, Apache Spark/Scala, and equivalent multi-cloud services
- Manage and optimize a growing cloud-based data ecosystem, ensuring the reliability and performance of corporate data lakes and analytics data marts
- Collaborate with cross-functional teams to define and implement data strategy, including data acquisition, ingestion, and integration from diverse sources and formats
- Develop and deploy custom data pipelines that support machine learning models, business intelligence dashboards, and new data products
- Implement data quality assurance practices using automated validation frameworks such as dbt or Great Expectations
- Establish metadata management standards, including data lineage and stewardship for high-value datasets
- Identify and implement automation opportunities leveraging AI capabilities and advanced data modeling to enhance data workflows and business outcomes
- Enforce strong development standards through code reviews, testing, and monitoring
- Stay current with emerging trends in data engineering, AI, and cloud technologies to continuously improve data solutions and align with Marsh’s data strategy
- Evangelize best practices in data engineering and AI adoption across the organization
Requirements
What you’ll need- BE /B.Tech /MCA /BSc.(IT)
- 6+years of experience
- Advanced proficiency in SQL and Python
- Hands-on experience with cloud data platforms, preferably multi-cloud (Azure, AWS, GCP)
- Strong knowledge of data pipeline frameworks and big data technologies such as Apache Spark and Databricks
- Familiarity with AI/ML concepts and practical applications in data engineering
- Experience with data quality frameworks and metadata management
- Professional certifications in Databricks, MS Azure, AWS, GCP etc.
- Experience with automation and AI-driven data engineering tools
- Knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes)
- Exposure to modern data governance and security practices
Benefits
Comp & perks- Professional development opportunities
- Interesting work
- Supportive leaders
- Vibrant and inclusive culture
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
SQLPythonApache SparkDatabricksdata pipeline frameworksbig data technologiesdata quality frameworksmetadata managementautomationAI-driven data engineering
Soft Skills
collaborationcommunicationproblem-solvingleadershiporganizational skillsadaptabilitycritical thinkingattention to detailevangelizing best practicesstrategic thinking
Certifications
Databricks certificationMS Azure certificationAWS certificationGCP certification