- Create scalable data pipelines using Glue, Eventbridge, DynamoDB to process life insurance data and support data modeling for policy records, claims, actuarial datasets.
- Write and optimize ETL and reconciliation workflows using PySpark and Python to transform raw policyholder data into analytics-ready datasets for reporting and decision-making.
- Build and implement event-driven data solutions on AWS, leveraging SQS, SNS, and API integrations to enable real-time processing of insurance transactions and policy events
- Implement snowflake data masking and row level security policies to ensure sensitive policyholder and beneficiary data are protected.
- Create Splunk data pipelines and PowerBI dashboards to ingest, index and analyze structured and unstructured life insurance data including policy transactions, claims events enabling real-time operational monitoring.
- Build flow zones and manifest file generator in Dataiku to develop data processing logic that meets life insurance business and regulatory requirements
- Write and optimize ETL workflows using data-oriented programming languages such as PySpark and Python to extract and transform raw policyholder data and support machine learning model development by preparing high-quality, analytics-ready datasets for reporting and decision-making.
- Write SQL queries and stored procedures in Amazon redshift to support life insurance reporting, premium reconciliation, and claim analytics and implement optimized data modeling techniques for analytical workloads.
- Develop end-to-end data workflows in Dataiku, integrating multiple sources to extract and analyze life insurance data such as policy administration systems, claims databases and actuarial feeds for business insights.
- Write and optimize Delta Lake table implementations in Databricks to support efficient storage, ACID transactions, and time travel queries on life insurance datasets.
- Build and maintain alteryx server scheduled workflows to automate end-to-end data processing pipelines for life insurance data and enable data visualization for claims reporting on Tableau.
- Work on Jira tickets for the sprint plan and utilize Gitlab repositories and confluence and participate in retrospective meetings
Minimum Education Requirement:- This position requires, at a minimum of a bachelor’s degree in computer science, computer information systems, information technology, relevant engineering, (computer engineering, software engineering, electronic engineering or related) or a combination of education and experience equating to the U.S. equivalent of a Bachelor’s degree in one of the aforementioned subjects.
