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Entry-level, popular Data Engineer title, metro location, and known employer increase candidate competition.
Core data engineering skills are broadly transferable across industries and domains.
Requires degree plus specific data platform and governance skills but no strict years, so moderate filtering.
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Develop and maintain enterprise data products by building data pipelines, transformations, and curated datasets supporting reporting, analytics, automation, and GenAI use cases across multiple enterprise 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 and consistent business outcomes.
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 transformations with modern data platforms and cloud technologies.
Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance within 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.
Has experience or exposure to a Data-as-a-Product operating model including use of data catalogs, lineage, data quality frameworks, and certified data products.
Understands AI/ML or GenAI initiatives with experience preparing AI-ready datasets and semantic models for intelligent business solutions.
Operates well in Agile, cross-functional teams collaborating with product, engineering, analytics, and business stakeholders.