• Data Architecture: Design, develop, and implement scalable, secure, and efficient data solutions that meet the needs of the organization. • Data Modeling: Create and maintain logical and physical data models to support business intelligence, analytics, and reporting requirements. • Pipeline Engineering: Design, build, and optimize ETL (Extract, Transform, Load) processes and data pipelines to ensure smooth and efficient data flow from various sources. • Data Integration: Integrate diverse data sources, including APIs, databases, and third-party data, into a unified data platform. • Performance Optimization: Monitor and optimize the performance of data systems and pipelines to ensure low latency and high throughput. • Data Quality and Governance: Implement data quality checks, validation processes, and governance frameworks to ensure the accuracy and reliability of data. • Collaboration: Partner closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet their needs. • Documentation: Maintain comprehensive documentation of data architectures, models, and pipelines for ongoing maintenance and knowledge sharing. • Training: You'll train and collaborate with teammates effectively in data engineering best practices • Technical Influence/Leadership: Recommends policy changes and establishes department-wide procedures. Uses extensive experience and knowledge to resolve complex problems. • Monitor and manage production environment to deliver data within defined SLAs

• Serve as a technical lead and de facto anchor for the data engineering team — setting the standard for code quality, system design, and engineering judgment • Own complex, ambiguous data engineering problems end-to-end, from architecture through deployment and ongoing reliability • Mentor and level up junior and mid-level engineers through code reviews, design discussions, and direct feedback • Partner with Product Managers and cross-functional stakeholders to translate business requirements into scalable, maintainable technical solutions • Drive architectural decisions across infrastructure, application, and data tiers — and be accountable for their outcomes • Identify and lead initiatives to improve system performance, reliability, and engineering efficiency across the team • Participate in all stages of the software development life cycle, from design and development through deployment and maintenance • Implement comprehensive testing and performance tuning to ensure system stability and uptime

• You’ll lead client discovery to understand business goals, data landscape, constraints, and success metrics—then translate findings into a clear delivery plan. • You’ll design and recommend data lake, Lakehouse, data warehouse, and analytical data store architectures on Azure and Microsoft Fabric aligned to client needs. • You’ll implement (and/or guide implementation of) ingestion, transformation, and orchestration pipelines using Microsoft Fabric experiences and Azure services (e.g., Data Factory, Synapse, Databricks). • You’ll integrate data from operational systems, APIs, and third-party sources and transform raw data into curated datasets ready for consumption. • You’ll build and maintain analytics-ready data models and schemas; implement enrichment, business rules, and reusable patterns for consistency across projects. • You’ll enable multiple compute engines (SQL, Spark/PySpark, Spark SQL, KQL) to run against shared datasets, supporting diverse analytics scenarios. • You’ll design and build AI-enabled analytics and data products, including agentic workflows (e.g., retrieval-augmented generation patterns), ensuring solutions are scalable, secure, and aligned with responsible AI practices. • You’ll establish data quality practices (validation, reconciliation, testing) and monitoring, troubleshoot failures, and continuously improve pipeline resiliency. • You’ll optimize transformations, query performance, and pipeline execution to meet client SLAs and cost objectives. • You’ll implement logging, metrics, and operational runbooks, and support deployments, cutovers, and hypercare as needed. • You’ll design and implement security, governance, and compliance controls (access, least privilege, data protection, lineage) across Azure and Fabric. • You’ll facilitate workshops and communicate architecture tradeoffs, partnering with client stakeholders, architects, analysts, and BI developers to deliver trusted datasets. • You’ll produce clear consulting deliverables (design docs, diagrams, implementation guides) and enablement materials to support adoption and smooth handoff.

• Design and maintain conceptual, logical, and physical data models for the Medicaid EDW. • Define architecture standards for ODS, integration, dimensional, and reporting layers. • Lead source system onboarding into the EDW, including claims, eligibility, provider, encounters, finance, and other Medicaid domains. • Review new initiatives for data impacts, scalability, integration patterns, and downstream reporting implications. • Recommend improvements to warehouse performance, data structures, and overall platform architecture. • Administer and enhance the enterprise Dataedo platform. • Maintain data dictionaries, table/column definitions, business glossary, lineage, and stewardship assignments. • Build and govern source-to-target mapping documentation. • Ensure metadata standards are followed across projects and support teams. • Publish user-friendly documentation portals for business and technical users. • Establish governance processes for definitions, ownership, approvals, and change management. • Maintain end-to-end lineage from source systems through ETL pipelines to reporting assets. • Support impact analysis for schema changes, source changes, and reporting requests. • Partner with data owners and SMEs to standardize KPI and metric definitions. • Support reporting and traceability requirements for Medicaid operations and regulatory reporting. • Help ensure data assets meet privacy, security, and compliance requirements including HIPAA. • Assist with audit requests requiring data lineage, documentation, and control evidence. • Partner with ETL developers, BI teams, analysts, QA teams, DBAs, and business stakeholders. • Mentor technical staff on modeling standards, documentation expectations, and governance practices. • Participate in roadmap planning, release planning, and architecture reviews.