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About This Role
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.
What you’ll do:
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.
Job 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.
Qualifications:
Basic Qualifications:
- 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.
Preferred:
- Advanced degree a plus.
- Experience in a global manufacturing or industrial environment, including plant-level data, is preferred.
Skills:
- 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
All positions may require participation in video and in-person interviews as part of the hiring process. All candidates will be evaluated based on job-related competencies, and all candidates’ privacy rights and data security will be protected in accordance with applicable laws.
The application window for this position is anticipated to close on 10/21/26
We are committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job-related reasons regardless of an applicant's race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, marital status, genetic information, protected veteran status, or any other status protected by law.
Eaton believes in second chance employment. Qualified applicants with arrest or conviction history will be considered regardless of their arrest or conviction history, consistent with the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act and other local laws.
You do not need to disclose your conviction history or participate in a background check until a conditional job offer is made to you. After making a conditional offer and running a background check, if Eaton is concerned about conviction that is directly related to the job, you will be given the chance to explain the circumstances surrounding the conviction, provide mitigating evidence, or challenge the accuracy of the background report.
To request a disability-related reasonable accommodation to assist you in your job search, application, or interview process, please call us at 1-800-836-6345 to discuss your specific need. Only accommodation requests will be accepted by this phone number.
We know that good benefit programs are important to employees and their families. Eaton provides various Health and Welfare benefits as well as Retirement benefits, and several programs that provide for paid and unpaid time away from work. Click here for more detail: Eaton Benefits Overview. Please note that specific programs and options available to an employee may depend on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements.
Salary Context
This $146K-$215K range is above the 75th percentile for Data Engineer roles in our dataset (median: $140K across 33 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,079 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Eaton, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,000 based on 180 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $146K to $215K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Eaton AI Hiring
Eaton has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Menomonee Falls, WI, Houston, TX, US, Beachwood, OH. Compensation range: $215K - $215K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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