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Common Data Engineer title, broad skillset, and metro role increase competition.
Moderate transferability; enterprise data-platform focus and domain-specific datasets favor domain-experienced candidates.
Moderate due to seniority and technical toolset requirements but no explicit years or certifications.
Lead design, development, and maintenance of scalable data and analytics platforms including large-scale data storage and processing solutions across various domains such as Supply Chain, Quality, Finance, and Product Lifecycle.
Design, build, and optimize reusable data pipelines and domain-aligned data products with metadata, data quality controls, and documentation supporting analytics, APIs, automation, and GenAI use cases.
Collaborate with cross-functional teams (Product Managers, Data Scientists, Architects, business stakeholders) in an Agile environment to deliver AI-ready datasets and enterprise-wide reusable data assets.
Bachelor’s degree in Computer Science, IT, Engineering, Data Analytics or equivalent practical experience.
Intermediate experience in data engineering including development and support of enterprise data pipelines, ETL/ELT, data modeling, SQL, and scalable data architectures.
Familiarity with Big Data technologies such as Spark, Scala/Java, Map-Reduce, Hive, Hbase, Kafka or equivalent.
Experience working in Agile teams and collaborating with multiple stakeholders to deliver data solutions.
Experienced in designing and implementing data-as-a-product principles including metadata management, lineage, governance, and data cataloging.
Proficient in leveraging modern cloud data platforms and ETL/ELT tools to create scalable, maintainable data solutions supporting AI/ML and analytics use cases.
Effective collaborator able to translate complex business requirements into technical solutions that balance quality, performance, and scalability, particularly in cross-functional Agile teams.