Data and AI Platform Engineer

Job Description:

The purpose of the Data Architect - Staff role is to design, implement, and maintain both traditional and modern data infrastructures at the staff level. This involves combining expertise in relational databases, cloud technologies, and big data tools to create scalable, efficient, and secure data solutions. The role also includes developing strategies for integrating enterprise data, ensuring data quality, and optimizing data access across both cloud and on-premise systems

We are committed to offering flexible work options where approved and stated in the job posting. However, we are currently not considering candidates who reside or plan to reside in the following states: California, Connecticut, Hawaii, Illinois, Massachusetts, Minnesota, New York, Pennsylvania, Rhode Island, Vermont, Virginia and Washington. Colorado for remote caregivers’ whose assigned Intermountain facility or service area is not based in Colorado.

Please note that a video interview through Microsoft Teams will be required as well as potential onsite interviews and meetings

Essential Functions

• 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

Skills

• Data architecture and design
• Cloud infrastructure and data solutions
• ETL/ELT development and data integration
• Database and performance optimization
• Data modeling and documentation
• Coding/Programming
• Data governance and security
• Collaboration and stakeholder management
• Leadership and project management

Qualifications

Minimum Qualifications

• 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.

Preferred Qualifications

• 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, monito…