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Metro location and mid-level generalist title raise competition; specialized Spark requirements moderate applicant density.
Big-data engineering skills are transferable, but Spark/platform leadership requires domain-specific experience.
Mandatory deep Spark, cloud, leadership, and platform experience create rigorous screening.
Lead and manage a team of 5-10 software and data engineers focused on delivering large-scale distributed data processing capabilities and platform modernization using Apache Spark.
Own technical oversight including architecture, design, development, optimization, and operational excellence of scalable Spark and big data workloads in cloud environments (AWS/GCP).
Collaborate cross-functionally to translate business needs into engineering plans, manage delivery risks, conduct performance reviews, and drive continuous process improvement.
Strong hands-on expertise with Apache Spark technologies and distributed data processing systems.
Proficiency in programming languages such as Scala, Java, or Python.
Experience deploying and managing Spark workloads on AWS and/or GCP cloud platforms.
Work Experience Required: Proven experience leading engineering teams and managing complex Spark-based data platform projects; exact years not explicitly mentioned.
Hybrid work location requiring minimum two days per week onsite at a TransUnion office.
Experienced mid-level engineering manager with proven ability to balance technical leadership and people management in big data environments.
Demonstrated success in delivering scalable, fault-tolerant Spark data processing platforms operating at terabyte-to-petabyte scale in cloud-native architectures.
Strong collaborator capable of driving technical strategy, mentoring teams, and overseeing multi-disciplinary stakeholder engagement including product, architecture, and data science.