Job Summary

Design and optimize cloud based data platforms that power analytics and decision making for a global property and casualty insurance business using AWS PySpark Python and Databricks within a hybrid work model ensuring resilient data pipelines reliable governance and scalable architectures that enable accurate risk assessment and faster claims processing.

Responsibilities

• Design and implement scalable data architectures on AWS that support end to end data flows for property and casualty insurance underwriting pricing claims and policy administration use cases
• Build robust PySpark and Python based data pipelines in Databricks that ingest transform and curate structured and semi structured insurance data from multiple internal and external sources
• Optimize Databricks workflows and Spark jobs to achieve predictable performance cost efficiency and reliability for large volumes of policy premium exposure and claims data




• Develop reusable data models and canonical data sets that standardize property and casualty insurance entities such as policies coverages claims and reinsurance treaties across business lines
• Establish and maintain data quality rules data validation checks and reconciliation processes that improve accuracy timeliness and completeness of critical regulatory and financial reporting datasets
• Collaborate with actuaries underwriters claims analysts and product teams to translate analytical and reporting requirements into well defined data engineering solutions that create measurable business value
• Implement secure data access patterns on AWS using appropriate services and configurations that protect sensitive customer and financial information while enabling governed self service analytics
• Create and maintain technical documentation including data flow diagrams data dictionaries and architecture blueprints that provide transparent traceability from source systems to curated consumption layers
• Automate testing monitoring and alerting f

📌 Data Engineer (Chennai)
🏢 Cognizant
📍 Chennai