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Generalist Data Engineer title and broad platform skill requirements increase applicant density.
Core data engineering skills are highly transferable across industries, lowering background sensitivity.
Broad required data engineering skills but no explicit years or certifications imply moderate screening.
Develop and maintain scalable enterprise data products and pipelines supporting analytics, AI, automation, and GenAI use cases across multiple enterprise domains.
Implement Data-as-a-Product principles to ensure data assets are reusable, discoverable, governed with metadata, lineage, and quality control.
Collaborate cross-functionally with Product Managers, Engineers, and Data Scientists to deliver AI-ready data products optimized for machine learning and analytics.
Experience in developing and supporting data integration, ETL/ELT processes, data pipelines with modern cloud-based data platforms.
Proficient in 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 but not explicitly quantified; exposure can include internships, co-ops, or academic projects.
Experienced in working within Agile, cross-functional teams collaborating with product, engineering, analytics, and business stakeholders.
Familiarity or experience applying Data-as-a-Product operating models including catalogs, lineage, and certified data products.
Knowledge or exposure to AI/ML or GenAI initiatives, especially in preparing AI-ready datasets and semantic models.