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Generalist Data Engineer role with broad skillset requirements increases applicant density.
Core data engineering skills are transferable, but enterprise data-product focus requires some domain knowledge.
Multiple required technical skills but no hard years requirement yields moderate shortlisting strictness.
Develop and maintain enterprise-scale data pipelines and transformations to support reporting, analytics, automation, and GenAI use cases across multiple domains such as Supply Chain and Finance.
Implement Data-as-a-Product principles ensuring data assets are reusable, discoverable, governed with metadata, lineage, and quality controls to enable self-service and consistent outcomes.
Collaborate with cross-functional teams including Product Managers, Data Engineers, and Data Scientists to deliver AI-ready, reliable, and optimized data products for analytics and machine learning applications.
Experience with developing and supporting data integration, ETL/ELT, data pipelines, and transformations on modern and cloud-based data platforms.
Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance for enterprise data products.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics, or equivalent experience.
Work Experience Required: Relevant experience preferred but not explicitly specified (internships, co-op, or extracurricular team activities considered).
Has hands-on experience with Data-as-a-Product models including data catalogs, lineage tracking, and data quality frameworks.
Experienced in collaborative Agile environments working with cross-functional teams on data platforms supporting AI/ML or GenAI projects.
Strong ability to translate business/product requirements into scalable, maintainable, and efficient technical data solutions.