Data Engineer

1) Design, build, and optimize reusable data pipelines, curated data assets, and domain-aligned data products that support analytics, operational reporting, APIs, automation, and GenAI use cases across Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains.

2) Apply Data-as-a-Product principles by developing scalable, governed, and discoverable data assets with appropriate metadata, lineage, quality controls, and documentation, enabling self-service consumption and enterprise-wide reuse.

3) Partner with Product Managers, Data Scientists, Solution Engineers, and business stakeholders to prepare and deliver AI-ready datasets, semantic models, vectorized content, and trusted knowledge sources that support GenAI, advanced analytics, and intelligent business solutions.

Required Skills, Education, or Experience

1) Strong hands-on experience developing and supporting enterprise data pipelines, data transformations, and data integration solutions using modern cloud data platforms, data warehouses, and ETL/ELT technologies.

2) Experience with data modeling, SQL development, data quality validation, performance optimization, and scalable data architecture supporting multiple consumer patterns including reporting, APIs, analytics, and AI/ML.

3) Ability to translate business and product requirements into reusable technical solutions while balancing data quality, performance, maintainability, and long-term scalability.

4) Experience working within cross-functional Agile teams and collaborating effectively with Product Managers, Data Scientists, Architects, and business stakeholders to deliver business outcomes.

5) Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.