Principal AI Data Engineer

Eaton’s Corporate Information Technology team is seeking a Principal AI Data Engineer to support our global Data office. This position can be based at one of our US IT hubs including Beachwood, OH, Menomonee Falls, WI, Moon Township, PA, Raleigh, NC, or Houston, TX. Candidates must be local and commutable to these offices, as relocation assistance is not available. Some travel is expected (10-20%).

The expected annual salary range for this role is $146000 - $215000 a year. This position is also eligible for a variable incentive program.

Please note the salary information shown above is a general guideline only. Salaries are based upon candidate skills, experience, and qualifications, as well as market and business considerations.

As the senior technical builder within AI & Data Management, the Principal AI Data Engineer turns governance architecture into production capabilities for data quality, observability, metadata, lineage and AI agents. Reporting to the Senior Manager - Data Governance, this role sets the technical standards for Eaton’s Data Governance Program.

The role owns Enterprise Data Health, Atlan catalog connections, Snowflake governance schemas, cross-system lineage, Data Governance AI agents and the AI-readiness certification pipeline. As a senior individual contributor with no direct reports, it also supports complex AI and plant data initiatives, sets sprint-level technical goals and raises engineering standards across the organization.

• Build and operate the enterprise data quality capability on Enterprise Data Health: DGO rule development, rule coverage across domains, and the executive data quality scorecard reported through a technical, AI and data quality lens.
• Build full-stack data observability with alerting and ticketing linked to the data quality framework, so issues are detected, routed and closed rather than only reported.
• Design, build, evaluate and operate the Data Governance AI agents, and act as product owner for the agent estate, including agent scope, evaluation and accuracy thresholds, decision traces on every run, and retirement criteria.
• Own all connections within the enterprise data catalog (Atlan): connection setup, hierarchy structure, scheduling, issue resolution, deprecation, and SDK and API work.
• Stand up, refresh and assure the accuracy of cross-system data lineage for all strategic platforms.
• Own and curate the Data Governance schemas within Snowflake.
• Engineer and run the AI-readiness certification pipeline that certifies data products as agent-consumable, including machine-readable definitions, explicit lineage and embedded permissions, executing against the certification standard set by the Principal Solution Architect.
• Build retrieval‑ready pipelines for unstructured data, including semantic cleaning, chunking and embedding, with entitlements carried in the metadata so retrieval cannot overshare.
• Set sprint‑level technical goals for the AI‑Native Data Engineering pod and provide functional technical direction to the Data Governance Engineers.
• Build and own the enterprise AI spend data pipeline so that a new AI platform can be onboarded in days rather than a quarter, replacing the person‑dependent desktop extract that currently feeds the AI Fin Ops Hub.
• Design and own the data flow for the Enterprise AI Catalog, resolving intake records, configuration management AI fields, agent and connectivity registries and platform inventories into a single AI asset identity, so that AI spend is attributable and inventory health is measurable.
• Provide senior data engineering depth to complex work outside the pod, including the data initiatives behind AI Navigator intake reporting and analytics, in partnership with the…