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Generalist data engineer role, metro location, mid-level expectations, and broad skillset requirements increase competition.
Core data engineering skills transfer easily across industries, so background sensitivity is low.
Multiple mandatory technical skills and data governance expectations but no explicit years requirement, so moderate filtering.
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Develop and maintain enterprise data products and pipelines supporting reporting, analytics, automation, and GenAI use cases across multiple business domains.
Apply Data-as-a-Product principles to ensure reusable, governed data assets with metadata, lineage, and quality controls for self-service consumption.
Collaborate with cross-functional teams (Product Managers, Data Scientists, Engineers) to deliver AI-ready, reliable, well-structured data optimized for analytics and machine learning.
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 practices, metadata management, and data governance for enterprise-scale data products.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics or equivalent experience.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in Agile cross-functional team environments with product, engineering, analytics, and business stakeholders.
Familiar with Data-as-a-Product operating models including data catalogs, lineage, and data quality frameworks.
Has exposure to AI/ML or GenAI initiatives, preparing datasets and data structures for intelligent business solutions.