TELECOMMUTE Director / Sr. Architect Data Engineering & AI (HealthSystems)

ScaleHealthTech is a 100% healthcare-exclusive technology and consulting firm, purpose-built to serve U.S. health systems and health plans. As a CHIME Foundation Partner and active HIMSS member, we continuously invest in understanding the priorities and challenges of CIOs, CDAOs, CDOs, CNIOs, CMIOs, and digital leaders. Our approach combines deep US healthcare leadership experience with disciplined offshore based execution, governance, and best-practice frameworks that enable enterprise-scale transformation for our customers.ScaleHealthTech is a boutique, fast growing company with a tier one consulting company feel. The founder and Chief Advisor are highly recognized, respected leaders in US Healthcare., ScaleHealthTech is seeking a highly experienced Director / Sr. Architect Data Engineering & AI to lead the design, governance, and delivery of production-grade data and AI capabilities for healthcare clients. Minimum 7 years of consistent experience in similar capacity such as Director of data architect, Director data analytics with the reputed Health Systems in the US. Must have worked with CDAOs, CIOs, VP Analytics, Chief AI Officers of the hospitals and the Health Systems. This is a part-time leadership role for someone who can operate at both strategic and execution levels, helping health systems move beyond AI pilots into governed, scalable, real-world outcomes.

The ideal candidate brings deep expertise in healthcare data engineering, cloud data platforms, FHIR-native architecture, AI/ML delivery, MLOps, and AI governance. This role is best suited for a hands-on director who understands clinical, operational, and revenue cycle use cases and can guide teams building trusted data foundations and production AI systems., * Lead data engineering and AI strategy for healthcare-focused client initiatives.

• Design scalable, secure, and governed data architectures across platforms such as Snowflake, Databricks, Azure Synapse, BigQuery, Microsoft Fabric, or similar.
• Guide FHIR-native data modeling, normalization, clinical terminology mapping, and longitudinal patient record design.
• Oversee ETL/ELT pipelines, streaming and batch data workflows, data quality, lineage, provenance, and master data management.
• Lead implementation of AI/ML and GenAI solutions including predictive models, risk scoring, clinical NLP, RAG architectures, agentic workflows, and healthcare automation use cases.
• Establish MLOps, LLMOps, and AgentOps practices including model registry, monitoring, drift detection, bias monitoring, audit trails, and governance controls.
• Partner with CIO, CDAO, product, engineering, clinical, compliance, and business stakeholders to convert roadmaps into production-ready outcomes.
• Support healthcare AI governance aligned with CHAI, NIST AI RMF, ONC HTI-1, HIPAA, SOC2, HITRUST, and responsible AI practices.
• Mentor data engineers, AI engineers, architects, and delivery teams while ensuring technical quality and delivery accountability.
• Help define reusable accelerators, reference architectures, delivery playbooks, and best practices for ScaleHealthTech’s Virtual GCC model., * Work in a flexible, part-time leadership role while contributing to high-impact healthcare transformation initiatives.
• Help health systems move beyond AI pilots into governed, production-ready data and AI solutions.
• Join a 100% healthcare-focused and digital health native consulting firm with deep expertise across health systems, payers, healthcare ISVs, interoperability, cloud platforms, data engineering, and AI strategy.
• Lead meaningful work across FHIR-native data platforms, predictive analytics, GenAI, agentic AI, MLOps, governance, and healthcare workflow automation.
• Partner with CIOs, CDAOs, clinical leaders, and technology teams to solve real healthcare challenges involving patient experience, revenue cycle, population health, and operational efficiency.
• Be part of ScaleHealthTech’s Virtual GCC model, helping clients build dedicated, scalable, healthcare-native data and AI capabilities at lower cost and faster speed.
• Contribute to a team focused on practical outcomes, trusted data, responsible AI, measurable ROI, and production-first delivery.

• 9-10+ years of experience in data engineering, analytics engineering, cloud data architecture, AI/ML engineering, or related fields.
• 3+ years in a leadership role such as Director, Senior Manager, Head of Data, Data Architect, AI Lead, or similar.
• Strong healthcare domain experience, preferably with health systems, providers, payers, digital health, revenue cycle, clinical data, or population health.
• Hands-on understanding of FHIR, HL7, claims data, EHR data, LOINC, SNOMED, RxNorm, HEDIS, and healthcare interoperability standards.
• Strong experience with modern data platforms such as Snowflake, Databricks, BigQuery, Azure Synapse, Microsoft Fabric, or AWS/Google Cloud Platform/Azure data services.
• Experience with data pipelines and orchestration tools such as Spark, Kafka, Airflow, dbt, Fivetran, Informatica, or similar.
• Working knowledge of AI/ML delivery, MLOps, model deployment, monitoring, model governance, and production AI lifecycle management.
• Experience with GenAI, LLMs, RAG, vector databases, clinical NLP, or agentic AI workflows is strongly preferred.
• Strong communication skills with the ability to advise executives while also guiding technical teams.
• Ability to work effectively in a part-time or fractional capacity while owning outcomes and maintaining delivery momentum.

Preferred Qualifications

• Experience building or leading a Data & AI Center of Excellence.
• Experience with healthcare AI governance, responsible AI frameworks, PHI handling, tokenization, and compliance-heavy environments.
• Experience delivering AI use cases such as ambient documentation, prior authorization, denial management, predictive population health, unified patient records, or clinical decision support.
• Prior consulting, GCC, captive center, or client-facing delivery leadership experience., You are a senior data and AI leader who understands that healthcare AI does not fail because of models alone. It fails when data quality, governance, workflow integration, and production engineering are missing. You know how to build trusted data foundations, lead accountable delivery teams, and turn AI roadmaps into secure, measurable, production-grade systems.