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Popular data-engineer title and cloud skills increase candidate density despite modest employer brand.
Core data engineering skills (ETL, SQL, pipelines) are highly transferable across industries.
Extensive mandatory cloud, ETL, and data platform skills create strict technical filters despite no explicit years.
Lead design, development, and maintenance of distributed and cloud-based enterprise data pipelines and analytics platforms.
Design, build, and optimize reusable, scalable, governed data products supporting analytics, APIs, AI/ML, and GenAI use cases across multiple enterprise domains.
Collaborate cross-functionally with Product Managers, Data Scientists, and business stakeholders to deliver AI-ready datasets and advanced analytical solutions.
Bachelor’s degree in Computer Science, IT, Engineering, Data Analytics or equivalent practical experience.
Intermediate work experience in relevant data engineering disciplines; familiarity with big data tools like Spark, Scala/Java, Hive, Kafka, and cloud data platforms.
Proven hands-on experience with ETL/ELT, SQL development, data modeling, data quality validation, and scalable data architectures.
Experience working in Agile cross-functional teams with strong collaboration among Product Managers and Data Scientists.
Experienced in building and operating large-scale, cloud-based data platforms with strong focus on data-as-a-product principles including metadata, lineage, and governance.
Skilled in translating business requirements into reusable and scalable technical data solutions balancing quality and performance.
Familiar with AI/ML and emerging technologies such as GenAI, vector databases, semantic search, and AI-ready data engineering practices.