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Popular generalist Data Engineer role with broad skill requirements increases competition.
Core data engineering skills (ETL, Spark, SQL, cloud) are broadly transferable across industries.
Moderate technical filters (ETL, Spark, cloud, governance) but no explicit years requirement.
Develop and maintain enterprise data products and pipelines to support reporting, analytics, automation, and AI 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.
Collaborate with cross-functional teams including Product Managers, Data Scientists, and Engineers to ensure data reliability and optimization for analytics and AI applications.
Experience in data integration, ETL/ELT processes, data pipelines, and transformations using modern data platforms and cloud technologies.
Working knowledge of data modeling, SQL, data quality, metadata management, and data governance principles.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Not explicitly mentioned in the JD.
Experience working in Agile, cross-functional teams collaborating with product, engineering, analytics, and business stakeholders.
Familiarity with Data-as-a-Product operating models including data catalogs, lineage, and quality frameworks.
Exposure or experience with AI/ML or GenAI initiatives, and preparing AI-ready datasets for intelligent business solutions.