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Generalist Data Engineer title plus broad skillset and mid-level seniority amplifies competition.
Core data engineering skills are moderately transferable across industries, giving medium background sensitivity.
No explicit years but requires domain skills and tool exposure, producing moderate screening filters.
Develop and maintain enterprise data platforms and pipelines supporting analytics, automation, and generative AI use cases across multiple business domains such as Supply Chain, Quality, and Finance.
Implement data governance processes including metadata management, data quality monitoring, and ensure data products meet business and technical requirements for scalability and reliability.
Collaborate with cross-functional teams (Product Managers, Data Scientists, Engineers) in Agile environments to deliver AI-ready, governed, and reusable data assets optimized for analytics and machine learning.
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Experience developing and supporting data integration, ETL/ELT processes, data pipelines, and data transformations using modern and cloud-based data platforms.
Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance principles at enterprise scale.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in applying Data-as-a-Product principles, delivering certified, discoverable, and well-governed data products with lineage and quality controls.
Familiar with Agile software development in cross-functional teams involving product, engineering, and analytics stakeholders.
Has exposure to AI/ML or Generative AI initiatives, including preparing AI-ready datasets and semantic or knowledge models supporting intelligent business applications.