Agentic Data Engineer - Health - Manager - Consulting - Boston

EY is a global leader in assurance, consulting, tax, and transaction services. In this role, you will lead teams to design and deliver healthcare data platforms that support AI-ready data products, ensuring safe and scalable systems for healthcare organizations.

Responsibilities:

• Lead the design, engineering, and delivery of modern healthcare data platforms that ingest, harmonize, and productize structured, semi-structured, and unstructured data from EHR/EMR, claims, clinical, operational, financial, wearable, and consumer-facing sources
• Design governed, AI-ready healthcare data products aligned to payer and provider workflows, including identity, terminology, master data, reference data, semantic/metrics layers, feature stores, and reusable domain data assets
• Lead healthcare interoperability and integration workstreams across FHIR, HL7 v2, CDA/CCDA, DICOM, X12 EDI, APIs, event-driven integration, clinical integration engines, and health information exchange patterns
• Engineer the agentic AI data foundation, including retrieval pipelines, embedding and indexing strategies, hybrid search, vector-store operations, knowledge graphs, grounding datasets, agent-to-data and agent-to-system access patterns, tool schemas, and governed API contracts
• Codify healthcare privacy, security, consent, PHI minimization, provenance, audit logging, lineage, and regulatory controls into data pipelines and platform patterns
• Establish data-platform operating discipline, including SLAs/SLOs, data quality, observability, metadata management, DataOps CI/CD, release readiness, and cloud cost discipline
• Own delivery accountability across discovery, architecture, build, test, release, run, and hand-off, including quality gates, RACI, risk escalation, engagement economics, and continuous improvement
• Coach and manage multidisciplinary teams of data engineers, interoperability engineers, analytics engineers, platform engineers, and AI/MLOps professionals
• Build reusable accelerators, reference architectures, and delivery IP that codify EHR/EMR, payer, interoperability, and agentic data engineering patterns

Requirements:

• Bachelor's degree and 6-10 years of experience in data engineering, data platforms, analytics engineering, AI-enabled data systems, or related technology disciplines
• 2-4 years of experience leading and developing technical teams, including data engineers, platform engineers, analytics engineers, interoperability engineers, or related delivery teams
• At least 2 years of healthcare industry experience supporting payer, provider, integrated delivery networks, academic medical centers, or health technology organizations
• Deep experience designing and implementing healthcare interoperability and data foundations, including FHIR, HL7 v2, X12, CDA/CCDA, healthcare terminology standards (SNOMED CT, LOINC, RxNorm), EMPI/MPI, common healthcare data models (OMOP, PCORnet, Sentinel), and evolving regulatory frameworks such as CMS-0057-F, TEFCA, USCDI, and Information Blocking
• Experience delivering healthcare data solutions across provider, payer, and consumer ecosystems, including clinical, claims, operational, financial, and consumer-facing data domains using both batch and real-time integration patterns
• Experience designing, implementing, and operating enterprise-scale cloud-native data platforms and lakehouse architectures that support healthcare interoperability, analytics, AI, and data products across platforms such as Databricks, Snowflake, Microsoft Fabric, AWS, Azure, or Google Cloud
• Strong proficiency in SQL, Python, Spark, and modern data engineering frameworks for ingestion, transformation, orchestration, testing, validation, and performance optimization
• Experience owning modern data engineering operating models including data products, data contracts, metadata management, observability, DataOps, CI/CD, Infrastructure-as-Code, and governance controls for enterprise data platforms
• Experience building AI-ready data foundations and integration architectures, including retrieval infrastructure, semantic layers, feature stores, vectorized data assets, knowledge graphs, and governed data products that support machine learning, generative AI, and agentic systems
• Experience implementing healthcare data governance, privacy, lineage, security, consent, and compliance controls, including HIPAA, HITRUST, de-identification, tokenization, auditability, and regulated healthcare data environments
• Experience translating business, clinical, operational, and technical requirements into scalable data platform architectures, implementation roadmaps, and delivery plans
• Ability to communicate architecture decisions, technical tradeoffs, risks, and recommendations to executive, clinical, operational, and technical stakeholders
• Willingness to travel to meet client obligations
• Experience working with major healthcare technology platforms and ecosystems, including Epic, Oracle Health/Cerner, Meditech, Athenahealth, Health Catalyst, Arcadia, or leading payer administration platforms
• Experience with healthcare-specific cloud and data services, such as AWS HealthLake, AWS HealthOmics, Azure Health Data Services, Google Cloud Healthcare API, Databricks healthcare solutions, Snowflake healthcare accelerators, or Microsoft Fabric healthcare patterns
• Experience implementing emerging agentic interoperability standards and frameworks, including MCP, agent tool registries, governed tool-access patterns, or other approaches for connecting AI agents to enterprise healthcare data and systems
• Master's degree in Computer Science, Data Engineering, Biomedical Informatics, Health Informatics, Engineering, Mathematics, or another quantitative or technical field
• Professional certifications in Databricks, Snowflake, AWS, Azure, Google Cloud, Microsoft Fabric, or other cloud and data platforms