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Popular generalist data-engineer role with broad skill requirements increases candidate competition.
Core data engineering skills are widely transferable across industries, so background sensitivity is low.
Moderate due to many required technical skills, governance and cloud experience but no strict years mandate.
Develop and maintain enterprise data products by building scalable data pipelines, transformations, and curated datasets to support analytics, automation, and GenAI use cases across multiple business domains.
Implement Data-as-a-Product principles to ensure data assets are reusable, discoverable, governed with metadata, lineage, and quality controls for consistent self-service consumption.
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 ETL/ELT data pipelines and transformations on modern data platforms and cloud environments.
Working knowledge of data modeling, SQL, data quality, metadata management, and enterprise data governance principles.
Bachelor's degree in Computer Science, IT, Engineering, Data Analytics, or equivalent practical experience.
Work Experience Required: Relevant experience preferred including internships or co-op; professional work experience not explicitly quantified in the JD.
Experienced in operationalizing Data-as-a-Product models with familiarity in data catalogs, lineage, and quality frameworks.
Comfortable translating complex business requirements into scalable, maintainable technical solutions within agile, cross-functional team settings.
Has exposure to AI, machine learning, or GenAI initiatives with skills in preparing AI-ready datasets and semantic models supporting intelligent business applications.