What you’ll do:

We are looking for a Senior Data Engineer to join the Market-to-Order (M2O) Commercial & Business Transformation team, part of Eaton's AI and Data Management Organization. This team delivers data and artificial intelligence (AI) products for Commercial Operations across Marketing, Sales, Quotes, and Order Entry by turning fragmented source data into governed, reusable Snowflake data products and AI capabilities that deliver measurable business outcomes.
This is a senior individual contributor role in a high-performing, forward-deployed data and AI engineering team that partners directly with business teams to deliver scalable, secure, and high-quality data solutions across business domains.

Demonstrate strong technical depth, ownership mindset, and leadership capability to deliver high-quality, enterprise-scale data products while mentoring team members and partnering with architects, analysts, product owners, and business stakeholders.
Take accountability for end-to-end data lifecycle activities, including data acquisition, ingestion, complex transformation, orchestration, data quality, data modeling, deployment automation, monitoring, support, and continuous improvement.
Assemble large, complex datasets that meet functional and non-functional requirements as well as enterprise technology and data protection standards.
Deploy complex enterprise solutions across data technology patterns and platforms, designing and orchestrating datasets with scale, supportability, and reusability at the forefront of design.
Demonstrate and document solutions using specifications, flowcharts, diagrams, code comments, source-to-target mapping, data lineage, and technical and business metadata.
Support modernization initiatives by improving legacy ETL patterns, reducing manual deployment steps, optimizing Snowflake workloads, and adopting scalable cloud data engineering practices.
Work in an Agile delivery model, including Scrum ceremonies, and collaborate directly with business stakeholders to deliver rapid, incremental business value and outcomes.
Engineer and optimize Snowflake and Azure data solutions using best practices for performance, high availability, role-based access, and cost-aware design.
Apply continuous integration and delivery, DevSecOps, and DataOps practices, including version control, automated deployment, rollback planning, and secrets management.
Embed data quality, observability, and production support into pipelines by analyzing root causes and implementing permanent fixes.
Deliver curated, governed datasets and interactive visualizations to business, reporting, and analytics teams.
Build and support the data foundation for artificial intelligence use cases and apply AI-augmented development practices with disciplined review and validation of generated code.
Champion specification-driven development using clear and testable specifications to guide design, build, and validation.
Lead design, code, and deployment reviews, establish reusable engineering patterns, and mentor engineers.

Qualifications:

• Bachelor's or Master's degree from an accredited institution in Computer Science, Information Technology, Engineering, or a related discipline.
• 12-16 years of experience in data engineering, data warehousing, ELT/ETL development, and cloud data platforms, including end-to-end delivery of production data pipelines at enterprise scale.

Skills:

• Advanced experience with one or more data storage and processing technologies, including data lake, warehouse, time series, and graph, on premises and in the cloud.
• Expert ability to interact with batch extract, transform, and load and on-demand data integration methodologies, including application programming interfaces and SQL.
• Demonstrated experience with cloud-based data storage and processing technologies, including object storage, databases, Spark, massively parallel processing warehouses, and containers.
• Experience with dimensional, transactional, medallion, lakehouse, and warehouse data modeling patterns, including star schema, Snowflake, Data Vault, slowly changing dimensions, third normal form, OLTP, OLAP, NoSQL, and big data technologies.
• Strong SQL proficiency with database concepts, data warehousing, performance tuning, query optimization, indexing, partitioning, and production troubleshooting.
• Advanced programming ability in Python, Scala, SQL, and PL-SQL for developing and supporting data pipelines, including code, orchestration, quality, and observability.
• Experience implementing data quality checks, reconciliation controls, audit logging, error handling, restartability, and observability instrumentation in production environments.
• Advanced data visualization experience in Power BI, Grafana, R, or Python to create interactive analytics solutions.
• Experience with DevSecOps, DataOps, continuous integration, and continuous delivery principles and tools.
• Strong hands-on experience with Snowflake and Azure data services, including Azure Data Factory and Azure Data Lake Storage.
• Experience with enterprise job scheduling and orchestration using Control-M or an equivalent platform.
• Working knowledge of artificial intelligence and agentic concepts, including agent orchestration, tool and function calling, prompt and context engineering, retrieval-augmented generation, embeddings, and vector stores.
• Capability in AI-augmented development using artificial intelligence coding assistants to accelerate development, testing, documentation, and troubleshooting with disciplined human validation and secure usage practices.
• Effective communication and presentation skills, with the ability to translate business requirements into scalable technical designs.
• Ownership mindset with the ability to manage technical risks, dependencies, and delivery commitments across multiple initiatives.
• Ability to mentor and influence engineers, architects, analysts, product owners, and business stakeholders without direct authority.
• Strong collaboration skills to work with global stakeholders across time zones.