EY - GDS Consulting - AIA - Agentic AI - Manager

The opportunity

Healthcare is in a moment of reimagination where AI is moving from experiments to live decisions —the next wave will not be won by agentic models that only demo well. It will come from production-grade AI systems that can reason over trusted healthcare data, retrieve the right context, act within governed workflows, explain their outputs, and earn trust across complex payer and provider environments.

Our Artificial Intelligence, Data and Engineering team helps healthcare organizations build the data foundation required for agentic systems to operate safely at scale. In this role, you will lead teams that connect healthcare's most critical data domains into governed, AI-ready data products and context layers, giving agents and applications the grounded, trustworthy inputs they need to reason and deliver value.

You can expect significant client interaction, hands-on technical leadership, and the opportunity to shape practical, scalable AI solutions for some of the most important payer and provider challenges in healthcare.

Your key responsibilities

You will lead multidisciplinary teams that design, engineer, and operate the healthcare data foundation required for production-grade agentic AI. You will translate complex healthcare business, clinical, regulatory, and technical requirements into scalable data architectures, governed AI-ready data products, secure interoperability patterns, and production delivery plans. Working closely with clients and EY teams, you will provide hands-on technical leadership, manage delivery risk, and communicate architecture decisions, tradeoffs, and recommendations to executive, clinical, operational, and technical stakeholders.

Your responsibilities will include:

• 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.

Skills and attributes for success

To excel in this role, you will need deep healthcare data engineering judgment, strong delivery leadership, and the ability to operate in complex, regulated environments where data quality, interoperability, privacy, trust, and speed all matter. You should be able to balance platform scalability, data usability, semantic consistency, retrieval quality, observability, cost, security, regulatory obligations, and business value.

The following attributes will make a significant impact:

• Healthcare-first data engineering mindset, with the ability to translate payer and provider workflows into trusted, governed, AI-ready data products that support analytics, automation, generative AI, and agentic systems.
• Practical architecture judgment across data platform patterns, including batch, streaming, event-driven integration, APIs, FHIR-native approaches, canonical data models, semantic layers, feature stores, knowledge graphs, vector indexes, and fit-for-purpose data products.
• Ability to make healthcare data usable, explainable, traceable, and safe for AI consumption through data quality controls, lineage, provenance, metadata, observability, access controls, PHI minimization, consent enforcement, and audit-ready documentation.
• Strong consulting leadership, including the ability to structure ambiguous healthcare data problems, facilitate workshops, manage delivery risk, coach multidisciplinary teams, and communicate architecture decisions, tradeoffs, risks, and recommendations to clinical, operational, technical, and executive audiences.

To qualify for the role you must have

• 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.
• Expe