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Mid-level data manager at a known multinational attracts moderate candidate density and competition.
Core data engineering skills (Spark, Kafka, Python, cloud) are broadly transferable across industries.
Technical stack and managerial responsibilities imply firm filtering on skills and leadership, but no explicit years requirement.
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Lead and manage data engineering teams to design, build, and maintain scalable data pipelines and platforms supporting enterprise analytics and reporting.
Translate data strategy and architecture into actionable engineering projects ensuring timely and high-quality delivery while enforcing data governance and compliance standards.
Oversee team development, budget management, and continuous improvement including performance optimization and adoption of best practices in data engineering.
Degree in relevant technical discipline or equivalent experience.
Experience managing and transforming large data sets using scalable data processing frameworks and modern data tools like Java, Python, Spark, Hive, Kafka, SQL, or cloud-native technologies.
Experience implementing and supporting data pipelines on cloud or distributed compute environments.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in collaborative environments working with data architects, analysts, and business partners to deliver integrated, scalable data solutions.
Proven capability to lead technical teams through Agile/iterative engineering practices and drive continuous improvement in cost, performance, and scalability of data pipelines.
Demonstrated expertise with security, compliance, and modern development practices including CI/CD, automated testing, and infrastructure as code.