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Metro-based, popular Data Engineer title with broad cloud and big-data requirements and mid-level expectations increases competition.
Core data engineering skills (ETL, Spark, SQL, cloud, pipelines) are highly transferable across industries.
Mandatory data platform, ETL, governance and cloud skills create moderate resume filtering despite no explicit years requirement.
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Develop and maintain enterprise data products by building and optimizing data pipelines and transformations supporting multiple business domains like Supply Chain, Finance, and Product Lifecycle.
Apply Data-as-a-Product principles to create governed, reusable, discoverable data assets with metadata, lineage, and quality controls for self-service and consistent outcomes.
Collaborate cross-functionally with Product Managers, Data Scientists, and Engineers to deliver AI-ready, reliable, well-structured data optimized for analytics, ML, and GenAI applications.
Experience developing and supporting data integration, ETL/ELT processes, and data pipelines using modern data platforms and cloud technologies.
Working knowledge of data modeling, SQL, data quality, metadata management, and enterprise data governance.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Relevant experience preferred but not strictly quantified; experience with Agile and cross-functional teams is expected.
Experienced in Data-as-a-Product operating models involving data catalogs, lineage, and certified data products.
Proficient in translating business and product requirements into scalable, maintainable data solutions with performance optimization.
Familiarity or exposure to AI/ML, GenAI data preparation, semantic models, or intelligent business data structures.