Data and AI Platform Engineer

Intermountain Health is seeking a Data and AI Platform Engineer to design, implement, and maintain traditional and modern data infrastructures. The role focuses on enterprise data integration, governance, quality, accessibility, secure platform services, and AI-enabled data capabilities across cloud and on-premise environments.

Responsibilities:

• Responsible for designing, implementing, and maintaining both traditional and modern data infrastructures at the staff level
• Combines expertise in relational databases, cloud technologies, and big data tools to create scalable, efficient, and secure data solutions
• Develops strategies for integrating enterprise data, ensuring data quality, and optimizing data access across both cloud and on-premise systems
• Works on moderate to complex projects and is mentored by senior-level data architects/engineers to deliver robust data models, pipelines, and warehouses that meet organizational needs using technologies like SQL, Python, PySpark, and traditional RDBMS platforms
• Design, build, and extend platform capabilities that enable enterprise data governance, including metadata management, lineage, stewardship, policy enforcement, and governance automation beyond vendor-provided functionality
• Build and maintain integrations between governance, data, analytics, AI, and operational platforms so metadata, classifications, ownership, quality signals, and governance decisions remain synchronized across the enterprise ecosystem
• Engineer the metadata, lineage, observability, and catalog capabilities of the Enterprise Data Platform, making enterprise data assets discoverable, understandable, traceable, and trusted for analytics, operations, and AI workloads
• Automate governance controls within the platform, including ownership assignment, certification, review workflows, policy enforcement, exception management, and audit evidence collection to reduce manual effort and improve compliance
• Apply AI, LLM, and agentic technologies to scale governance operations, including metadata generation, business glossary enrichment, data classification, stewardship recommendations, data quality monitoring, and intelligent remediation workflows
• Establish and operate the engineering practices that make AI-enabled governance trustworthy, including grounding on authoritative metadata, evaluation frameworks, quality measurement, confidence thresholds, monitoring, and continuous improvement
• Deliver secure and governed platform services that support analytics, AI agents, applications, and data products, ensuring enterprise standards for security, privacy, governance, and responsible AI are consistently applied
• Build platform capabilities that increase the effectiveness and reach of data stewards and governance programs, including workflow management, stewardship tooling, governance coverage reporting, operational dashboards, and scalability mechanisms
• Partner with data architecture, governance, analytics, engineering, cybersecurity, and vendor teams to translate governance objectives into sustainable platform capabilities, owning the full lifecycle from design and deployment through monitoring, support, and continuous enhancement
• Remain sitting or standing for long periods of time to perform work on a computer, telephone, or other equipment

Requirements:

• Proficiency with SQL and experience with traditional RDBMS (e.g., Oracle, SQL Server, PostgreSQL)
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform for data architecture and storage solutions
• Strong familiarity with programming languages such as Python and PySpark for data engineering tasks
• Working experience with ETL/ELT processes and tools, including both traditional (e.g., SSIS, Informatica) and/or cloud-native solutions (e.g., Azure Data Factory, Databricks)
• Proficiency with Python or PySpark or R programming
• Excellent communication skills for collaborating with stakeholders and teams
• Proficiency in Product Management, Project Management, or Program Management philosophies and methodologies, and capable of applying them to data architecture projects to ensure alignment with business goals and efficient execution
• Design, build, and extend platform capabilities that enable enterprise data governance, including metadata management, lineage, stewardship, policy enforcement, and governance automation beyond vendor-provided functionality
• Build and maintain integrations between governance, data, analytics, AI, and operational platforms so metadata, classifications, ownership, quality signals, and governance decisions remain synchronized across the enterprise ecosystem
• Engineer the metadata, lineage, observability, and catalog capabilities of the Enterprise Data Platform, making enterprise data assets discoverable, understandable, traceable, and trusted for analytics, operations, and AI workloads
• Automate governance controls within the platform, including ownership assignment, certification, review workflows, policy enforcement, exception management, and audit evidence collection to reduce manual effort and improve compliance
• Apply AI, LLM, and agentic technologies to scale governance operations, including metadata generation, business glossary enrichment, data classification, stewardship recommendations, data quality monitoring, and intelligent remediation workflows
• Establish and operate the engineering practices that make AI-enabled governance trustworthy, including grounding on authoritative metadata, evaluation frameworks, quality measurement, confidence thresholds, monitoring, and continuous improvement
• Deliver secure and governed platform services that support analytics, AI agents, applications, and data products, ensuring enterprise standards for security, privacy, governance, and responsible AI are consistently applied
• Build platform capabilities that increase the effectiveness and reach of data stewards and governance programs, including workflow management, stewardship tooling, governance coverage reporting, operational dashboards, and scalability mechanisms
• Partner with data architecture, governance, analytics, engineering, cybersecurity, and vendor teams to translate governance objectives into sustainable platform capabilities, owning the full lifecycle from design and deployment through monitoring, support, and continuous enhancement