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Medium: metro location, popular data-engineer title, and known employer increase candidate competition.
Low: core Spark, cloud, and data-platform engineering skills are broadly transferable across industries.
High: requires deep Apache Spark, cloud data-platform expertise plus proven engineering leadership and delivery experience.
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Lead and manage a team of 5–10 software and data engineers responsible for delivering scalable Apache Spark-based data processing platforms and feature enhancements.
Provide technical leadership in architecture, design, and optimization of large-scale distributed data systems using Spark and related cloud technologies (AWS/GCP).
Oversee delivery execution, technical roadmap, performance tuning, and operational reliability while collaborating with cross-functional stakeholders and supporting engineering talent growth.
Strong hands-on experience with Apache Spark (Spark Core, SQL, Streaming) and building/operating large-scale distributed data systems.
Experience in programming with Scala, Java, or Python for Spark workloads.
Proven leadership experience managing engineering teams and delivering complex technical initiatives.
Work Experience Required: Not explicitly mentioned in the JD (seniority implied mid-level management). Hybrid work mode: on-site minimum two days per week at TransUnion office.
Mid-level manager with technical depth in Spark distributed data processing and cloud platforms (AWS EMR, AWS Glue, GCP Dataproc, BigQuery).
Experienced balancing hands-on technical leadership with people management and cross-functional stakeholder collaboration.
Skilled in performance optimization of Spark workloads and driving engineering best practices, automation, and operational excellence at enterprise scale.