• 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

• Developing scalable ETL/ELT pipelines using Databricks, Spark/PySpark, and SQL • Designing and implementing data lakes, lakehouse architectures, and analytics platforms • Integrating data from multiple enterprise systems • Building analytics-ready datasets supporting BI, advanced analytics, and AI workloads • Optimizing Spark workloads, data models, and pipeline performance • Supporting data platform modernization and migration from legacy ETL solutions • Working in Agile teams with architects, analysts, and customer stakeholders • Contributing to engineering best practices, reusable patterns, and knowledge sharing