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Popular mid-senior data engineer role, broad tech requirements and metro hiring increase candidate competition.
Core data engineering skills transferable, but domain-specific enterprise data and governance needs increase industry sensitivity.
Strong mandatory technical skills but no explicit years requirement yields medium shortlisting strictness.
Lead design, development, and maintenance of scalable, automated data pipelines and analytics platforms supporting multiple product teams and domains such as Supply Chain, Finance, and Quality.
Drive implementation of Data-as-a-Product principles by creating governed, documented, and reusable data assets with monitoring and alerting for performance and quality.
Collaborate cross-functionally with Product Managers, Data Scientists, and business stakeholders to deliver AI-ready datasets, support advanced analytics, and enable GenAI use cases.
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 and integration solutions using cloud data platforms and ETL/ELT tools.
Proficiency in SQL development, data modeling, performance optimization, and scalable data architecture.
Work Experience Required: Intermediate experience in relevant discipline area (exact years not specified).
Experienced with large-scale data platforms and big data technologies such as Spark, Scala/Java, Map-Reduce, Hive, Kafka, and cloud-based clustered compute environments.
Familiarity with Data-as-a-Product operating models including metadata management, data cataloging, lineage, and governance.
Comfortable working Agile environments collaborating closely with cross-functional teams to translate business needs into robust, maintainable data solutions.