Principal AI Data Engineer

Eaton Corporation•Raleigh, NC

8h•$146,000 - $215,000•Hybrid

About The Position

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%). 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.

Requirements

Bachelor's degree from an accredited institution
Minimum 10 years of professional experience
Minimum 5 years of hands-on data governance experience (including data engineering, with demonstrated hands-on delivery of production data quality, metadata, lineage and pipeline capabilities on cloud data platforms, including experience building and/or operating AI or agent-based solutions)
Eaton will not consider applicants for employment immigration sponsorship or support for this position. This means that Eaton will not support any CPT, OPT, or STEM OPT plans, F-1 to H-1B, H-1B cap registration, O-1, E-3, TN status, I-485 job portability, etc.

Nice To Haves

Advanced degree a plus.
Experience in a global manufacturing or industrial environment, including plant-level data, is preferred.
Advanced SQL and Python; production pipelines optimized for performance and cost
Cloud data platforms: Snowflake (including Data Governance schema design and curation), Palantir, Dataiku, Azure AI Foundry
Enterprise data catalog and metadata engineering: Atlan connections, hierarchy, scheduling, deprecation, SDK and API work
Cross-system data lineage across strategic platforms, including build, refresh and accuracy assurance
Data quality engineering: rule design, DGO rule frameworks, cleansing, observability, alerting and ServiceNow-linked ticketing
AI agent engineering: build, maintenance, evaluation, accuracy scoring, decision traces, drift and hallucination monitoring, and retirement
AI-ready data engineering: machine-readable definitions, explicit lineage, embedded permissions, and retrieval-ready pipelines with metadata entitlements
MCP-based connectivity that exposes governed data and tools to agents within architecture standards
Power BI for data quality reporting and executive scorecards
DevSecOps and CI/CD, controls-as-code, and MuleSoft-based connectivity
Working knowledge of data and AI governance frameworks, including NIST AI RMF, ISO/IEC 42001, EU AI Act, as applied to engineered controls
Builds first; turns architecture and policy into working systems
Leads technically without formal authority and sets direction for other engineers
Communicates directly with engineers and leaders and demonstrates working capabilities
Works comfortably with plant teams and domain stewards to understand how data is created
Mentors engineers and raises engineering standards across the pod
Makes the safe path fast by automating support work
Partners across Master Data Management, Platforms, Architecture and the stewardship network
Navigates ambiguity in agentic AI, forms a point of view and acts

Responsibilities

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 FinOps 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 AI-native software engineering pod.
Conduct on-site data assessments at Eaton plants, trace data quality issues to their source systems and processes, and convert findings into rules, monitoring and remediation.
Build against the shared control-plane specification, reference architectures and integration patterns set by the Principal Solution Architect, and escalate pattern gaps rather than inventing local ones.
Train the Data Governance organization on new Snowflake and Atlan capabilities that change governance practice.
Partner with Master Data Management, Data and AI Platforms, Data and AI Architecture, and the federated stewardship network of domains, councils and stewards.