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Generalist Data Engineer title with broad, common skill requirements increases applicant competition.
Enterprise data engineering skills are transferable across industries but benefit from domain familiarity.
Strong mandatory technical skills (Spark, cloud, ETL) but no explicit years makes shortlisting moderately strict.
Lead design, development, and maintenance of scalable, automated data pipelines and distributed data platforms supporting analytics and AI use cases.
Design and implement data governance, quality monitoring, and metadata management for enterprise data products, ensuring data is reliable, discoverable, and compliant.
Collaborate with Product Managers, Data Scientists, and business stakeholders to deliver AI-ready datasets and data products following Data-as-a-Product principles in agile environments.
Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Intermediate experience in data engineering including designing and optimizing enterprise data pipelines using cloud platforms, ETL/ELT tools, and SQL.
Work Experience Required: Intermediate experience in relevant discipline; specific years not explicitly mentioned.
Familiarity with big data technologies (e.g., Spark, Hive, Kafka), cloud-based data solutions, and agile methodology; ability to work on-site with flexibility.
Experienced in building reusable, governed data products aligning with Data-as-a-Product frameworks to support advanced analytics and GenAI.
Able to translate complex business requirements into scalable, maintainable technical data solutions with strong data quality and performance focus.
Comfortable working cross-functionally within agile teams including Product, Data Science, and Engineering stakeholders to deliver measurable business outcomes.