• Build data applications and processes using Python, SQL, and Django; manage and query data in PostgreSQL, Oracle, and cloud-native databases • Examine, extract, cleanse, and load data while implementing quality assurance rules and tools to ensure consistent and accurate data • Work with healthcare-specific data processes such as EDI file transfers, claims adjudication, audits, eligibility verification, and reporting workflows • Collaborate with cross-functional teams (Data Analysts, Data Scientists, Product, Reporting, Account Management) to define requirements and deliver data-driven solutions • Ensure data quality, integrity, and security through automated validation, auditing, and monitoring, with compliance to HIPAA and CMS regulations • Monitor, maintain, and tune pipeline performance; proactively troubleshoot and resolve complex data flow and system issues • Provide technical mentorship to Data Engineers, sharing expertise in data modeling, pipeline development, and troubleshooting practices • Research and propose improvements to the tech stack and data engineering processes • Participate in sprint planning, refinement, and estimation to support implementation awareness and delivery

• Analyse and ingest new datasets from varied sources • Design, build, and support cloud infrastructure • Design, build, and support AI and ML processes and infrastructure • Design and build modern, metadata driven data pipelines and data streams • Design and build data service APIs • Transform and cleanse complex datasets • Analyse current business practices, processes and procedures • Implement effective metrics and monitoring processes

• Develop and maintain enterprise data models aligned with federal and state standards. • Design data integration strategies across multiple systems and agencies. • Ensure compliance with FISMA, FedRAMP, NIST, and applicable state data security frameworks. • Serve as a trusted advisor to federal and state clients, translating business needs into technical solutions. • Lead customer workshops, requirements sessions, and architecture reviews. • Communicate complex technical concepts clearly to non-technical stakeholders. • Establish data governance policies, metadata management, and stewardship practices. • Implement data quality frameworks and monitoring processes. • Architect solutions leveraging AWS, Azure, or GCP for government workloads. • Support migration from legacy systems to modern cloud-based platforms. • Partner with federal and state stakeholders, program managers, and technical teams to align data strategies with mission objectives. • Mentor junior architects and data engineers.

• Conduct technical screens and deep-dives for data/AI roles: SQL, Python, data modeling, ML theory, and applied system design. • Assess practical skills: feature engineering, evaluation metrics, leakage detection, experiment design, and tradeoff analysis. • Evaluate production readiness: monitoring, drift detection, CI/CD for ML, deployment patterns. • Provide clear, rubric-based feedback and participate in calibration and leveling. • Improve question banks, rubrics, and interview loops for data and AI roles.