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Metro location, popular Data Engineer title, and mid-level generalist skillset increase candidate competition density.
Core data engineering skills are transferable across industries, though enterprise domain knowledge moderately matters.
Multiple mandatory technical skills (ETL, Spark, SQL, cloud, governance) create moderate shortlisting filters.
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Develop and maintain enterprise-scale data products by building scalable data pipelines, transformations, and curated datasets supporting multiple business domains (Supply Chain, Quality, Finance, etc.).
Implement Data-as-a-Product principles ensuring reusable, discoverable, governed data assets with metadata, lineage, and quality controls for consistent business outcomes.
Collaborate with cross-functional teams (Product Managers, Data Engineers, Data Scientists) to deliver AI-ready data products optimized for analytics, machine learning, and GenAI applications.
Experience developing and supporting data integration, ETL/ELT, and data pipelines using modern data platforms and cloud technologies.
Working knowledge of data modeling, SQL, data quality, metadata management, and data governance at enterprise scale.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced with Data-as-a-Product operating models including data catalogs, lineage, and certified data products.
Familiarity with Agile development and cross-functional collaboration involving product and analytics teams.
Exposure to AI/ML or GenAI initiatives focusing on preparing AI-ready datasets and semantic models.