Description

Key Responsibilities
Build and maintain scalable ETL pipelines using PySpark, SQL, and Palantir Foundry
Work with data architects, analysts, and actuarial teams to interpret reinsurance data models
Transform and validate complex data sets for analytics, reporting & forecasting
Optimize data ingestion, processing, and transformation workflows
Implement strong data governance, lineage, and security practices
Identify & automate processes to enhance efficiency and scalability
Deliver high-performing and reliable data engineering solutions in a distributed environment

Required Skills & Qualifications
6–10 years of experience in Data Engineering
Hands-on expertise in PySpark (DataFrames, RDDs, performance tuning)
Working experience with Palantir Foundry or similar platforms
Strong SQL & Python capabilities
Experience with cloud platforms (AWS / Azure / GCP) and large-scale datasets
Solid understanding of reinsurance or insurance data structures (exposure, claims, policies)
Knowledge of metadata mgmt., data modeling, governance, and CI/CD
Familiarity with Git and Agile development practices

Preferred Skills
Reinsurance data warehousing & reporting modernization experience
Exposure to Palantir ontology design & data operationalization
API / REST & event-driven architecture exposure
Understanding of actuarial data & underwriting workflows is a plus

Key Skills

Education

Any Graduate