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Popular Data Engineer role with cloud/big-data skills attracts applicants, tempered by seniority and less-known employer.
Medium because core data engineering skills transfer across industries, but domain-specific data-product and supply-chain knowledge matters.
High due to mandatory modern data platform skills, cloud experience, data governance, and data-product requirements.
Lead design, development, and maintenance of scalable, efficient, and reliable data pipelines and large-scale data storage solutions supporting analytics, operational reporting, APIs, and AI/ML use cases.
Implement and enforce data governance, metadata management, and quality controls to enable discoverable, governed, and reusable data assets as part of a Data-as-a-Product model.
Collaborate cross-functionally with Product Managers, Data Scientists, and business stakeholders to deliver AI-ready datasets and support advanced analytics, GenAI, and intelligent business solutions.
Bachelor’s degree in Computer Science, IT, Engineering, Data Analytics, or equivalent practical experience required.
Intermediate experience in data engineering including developing and supporting enterprise data pipelines, data transformations, and data integration using modern cloud platforms and ETL/ELT technologies.
Technical skills required: SQL, data modeling, performance optimization, scalable data architecture; experience with Spark, Scala/Java, MapReduce, Hive, HBase, Kafka or equivalent is preferred but not mandatory.
Work Experience Required: Intermediate experience in relevant discipline required; explicit years of experience not stated.
Experienced working within Agile cross-functional teams involving Product Managers, Data Scientists, and Architects to translate business needs into reusable technical data solutions.
Familiar with Data-as-a-Product operational models including data cataloging, lineage, metadata, governance, and certification practices.
Has exposure or familiarity with GenAI, AI/ML data engineering practices, semantic search, vector databases, or AI-ready data preparation supporting enterprise AI solutions.