• Design, build, and optimize scalable data pipelines in Apache NiFi to automate ingestion, cleansing, and enrichment. • Architect and manage datasets on HDFS for high-volume storage. • Develop distributed processing workflows in PySpark and Hive. • Maintain and tune Postgres databases for high availability and performance. • Partner with Data Scientists to deliver clean datasets for model training. • Implement monitoring and alerting frameworks for pipeline health.

• Build, maintain, and optimize scalable data solutions to support Journey Analytics initiatives. • Maintain and refactor existing codebases, developing modular components. • Ensure high-quality, performant datasets for analytics and reporting use cases. • Maintain, optimize, and automate existing code repositories in GitHub. • Refactor legacy code to simplify maintenance, updates, and reuse across multiple use cases. • Design and build modular, reusable code components to support multiple journeys and reduce duplication. • Develop and manage automated data pipelines in Databricks to support Journey Analytics datasets and downstream reporting. • Consolidate key KPIs, metrics, and attributes into standardized data structures to enable flexible journey views. • Collaborate with analytics and engineering teams to improve data processes and architecture.

• Own and build the production-grade data layer that powers a Claims AI / Intelligent Suite running on Azure Databricks. • Design, build, and maintain production-grade data pipelines in Azure Databricks using Delta Live Tables and Structured Streaming. • Implement and operate medallion architecture (bronze, silver, gold) with clear data contracts, quality controls, and freshness SLAs. • Build and maintain scalable data models and feature tables for claims, policies, litigation, and adjuster data. • Engineer data preparation pipelines for AI workloads, including structured data serving and unstructured document processing for vector search and RAG use cases. • Enforce data quality, observability, and reliability through automated checks, lineage, schema enforcement, and freshness monitoring. • Own pipeline orchestration, CI/CD, monitoring, and failure recovery for production data systems. • Collaborate closely with AI Engineers and the Lead Databricks Architect to align data architecture with agentic AI and platform decisions. • Work with client data owners and platform teams to manage data access, upstream changes, and source system dependencies.

• Collaborate on the architecture and design of data solutions • Develop and maintain scalable ETL processes • Optimize data storage, processing, and analytics solutions • Ensure best practices in CI/CD and infrastructure as code • Work closely with cross-functional teams to deliver high-quality solutions