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Generic mid-level Data Engineer role with broad skillset requirements increases candidate competition.
Core data engineering skills are highly transferable across industries.
Technical skills and data platform experience required but no explicit years, so moderate filtering.
Develop and maintain enterprise data products through building and managing data pipelines and transformations supporting analytics, automation, and AI/GenAI use cases.
Implement Data-as-a-Product principles ensuring reusable, discoverable, governed data assets with metadata, lineage, and quality controls.
Collaborate with cross-functional teams including Product Managers, Data Engineers, and Data Scientists to deliver AI-ready data products 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, metadata management, and data governance for enterprise-scale data products.
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in implementing Data-as-a-Product operating models including data catalogs, lineage, and certified data products.
Familiar with AI/ML or GenAI initiatives, including preparation of AI-ready datasets and semantic models for intelligent business solutions.
Skilled at translating business/product requirements into scalable, maintainable, and reusable technical data solutions within Agile cross-functional teams.