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Leads enterprise data management implementations for insurance clients, including requirements gathering, data profiling, documentation, discovery and design sessions, solution configuration, data modeling, mappings, rules, reporting, and deployment. Analyzes large datasets using SQL in data warehouse environments, collaborates with client and internal teams, and supports strategic data informatics initiatives and executive presentations.

Serves as the domain authority for Security Master, Entity Master, and reference data management. Defines data standards, validation rules, quality frameworks, taxonomies, stewardship workflows, and data governance processes. Translates business requirements into technical models and APIs, rationalizes vendor data feeds, conducts profiling and quality assessments, supports QA testing, and guides system design and client workshops. Requires extensive financial instrument knowledge, vendor expertise, SQL proficiency, and experience with regulatory data standards.

Analyze business and data requirements, develop and optimize ETL processes, and support data integration, validation, reconciliation, business intelligence, and reporting. Ensure data quality across multiple sources, identify risks and improvement opportunities, collaborate with stakeholders and technical teams, and maintain documentation for data flows, mappings, and business rules.

Lead business intelligence reporting initiatives by gathering requirements, defining dashboard scope, mapping data sources, creating wireframes and specifications, and coordinating data engineering teams. Facilitate workshops, manage reporting backlogs, conduct functional testing and reconciliation, coordinate UAT and defect resolution, and oversee deployment and post-production validation. The role also contributes to data engineering strategy, innovation, executive presentations, and long-term data planning.

Designs and leads Security Master, Entity Master, and Reference Data MDM architecture. Responsibilities include data modeling, governance, golden records, match-merge and survivorship workflows, ingestion pipelines, APIs, data quality, lineage, AWS infrastructure, and integration patterns. The role produces architecture documents, leads design reviews, collaborates with technical teams and stakeholders, recommends MDM patterns, and mentors backend and data engineering teams.

Develop and support IBM InfoSphere MDM implementations end to end, including data modeling, customization, data quality, match and merge, survivorship, hierarchies, integrations, cloud migration, and production support. The role requires banking or financial services experience, AWS expertise, SQL, data governance knowledge, and Agile delivery experience. Preferred qualifications include DataStage or InfoSphere Information Server experience, ETL exposure, and mentoring or technical leadership.

Design, build, and maintain Angular front-ends and Python/Java back-end services; architect CI/CD and data pipelines for GenAI training/inference; troubleshoot full-stack and AI integrations; collaborate with cross-functional teams and mentor developers while promoting best practices.

Leads Neo4j graph database migrations to AWS, including architecture, secure deployment, data integration, performance tuning, query optimization, testing, demonstrations, developer onboarding, UAT, and go-live support. The role requires hands-on expertise with Neo4j, Cypher, AWS infrastructure, ETL tools, graph modeling, benchmarking, and stakeholder communication.

Designs and leads migration of Semantic Kernel plugins, planners, function calling, and memory to Microsoft Agent Framework agents and workflows. Rebuilds Python/Django orchestration while preserving APIs, permissions, and RAG functionality. Integrates MCP tools, deploys microservices on OpenShift, and implements observability, evaluation, CI/CD, DevSecOps, caching, and API gateway capabilities. Validates retrieval and agentic-system quality using Azure AI services, Arize Phoenix, and OpenTelemetry.

Design and develop Java Spring Boot backend microservices for a Master Data Management platform, including data ingestion, validation, match-merge, golden records, workflows, REST APIs, and downstream integrations. Build AWS-based processing and orchestration solutions, implement security controls, optimize performance, write tests, support containerized CI/CD deployments, and contribute to architecture, documentation, code reviews, and Agile delivery.