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Popular mid-level data engineering role, metro context, and broad skillset drive high applicant competition.
Core data engineering skills transfer across industries, though enterprise domain knowledge adds moderate bias.
Required modern data platform skills and data governance increase filter strictness despite no explicit years requirement.
Develop and maintain enterprise data products including data pipelines, transformations, and curated datasets supporting reporting, analytics, automation, and GenAI use cases across multiple business domains.
Implement Data-as-a-Product principles to create reusable, discoverable, and governed data assets with metadata, lineage, and quality controls enabling self-service consumption.
Collaborate with Product Managers, Data Engineers, Data Scientists, and Solution Engineers to deliver AI-ready data products optimized for analytics, machine learning, and GenAI applications.
Experience developing and supporting data integration, ETL/ELT processes, data pipelines, and data transformations using modern data platforms and cloud-based technologies.
Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance principles.
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Not explicitly mentioned in the JD.
Demonstrated ability to translate business and product requirements into scalable, performant, maintainable, and reusable technical data solutions.
Experience working in Agile, cross-functional teams collaborating with product, engineering, analytics, and business stakeholders.
Familiarity with Data-as-a-Product operating model, data catalogs, lineage, data quality frameworks, and AI/ML or GenAI initiatives including preparation of AI-ready datasets.