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Popular Data Engineer role, metro location, and recognizable employer increase competition for qualified applicants.
Core data engineering skills like ETL, SQL, and cloud are highly transferable across industries.
Domain experience and specific data platform skills create moderate filtering but no strict years or certifications required.
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Develop and maintain enterprise data products by building scalable data pipelines, transformations, and curated datasets supporting reporting, analytics, automation, and GenAI use cases.
Implement Data-as-a-Product principles to create governed, reusable, and discoverable data assets with metadata, lineage, and quality controls for self-service consumption.
Collaborate cross-functionally with Product Managers, Data Engineers, Data Scientists, and Solution Engineers to deliver AI-ready, reliable, and optimized data products for analytics and machine learning applications.
Experience developing and supporting data integration, ETL/ELT processes, data pipelines using modern data platforms and cloud technologies.
Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance for enterprise-scale data products.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Relevant experience preferred through internships, co-op, or equivalent; exact years not explicitly mentioned in the JD.
Experienced in building scalable, governed data products within large enterprise or cross-functional Agile environments.
Familiar with Data-as-a-Product operating models, including data catalogs, lineage, and certified data products.
Capable of translating business requirements into technical solutions optimized for performance, maintainability, and reusability in cloud or distributed data systems.