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Team Overview

Role Overview And Core Responsibilities

Design scalable and resilient data processing solutions using Apache Spark and modern data platform technologies. Create architecture designs, reference patterns, and technical specifications for distributed data platforms. Guide engineering teams on architecture, design, and implementation decisions. Ensure solutions align with enterprise architecture standards, security requirements, and engineering best practices. Evaluate technology choices, frameworks, and platform capabilities to address evolving business needs. Identify architectural risks and recommend mitigation strategies. Contribute to platform modernization and cloud transformation initiatives.

Provide technical leadership for Apache Spark-based batch and streaming data processing solutions. Guide development teams on Spark architecture, performance optimization, and operational best practices. Define design patterns for scalable, fault-tolerant, and maintainable Spark applications. Assist teams in resolving complex technical challenges involving distributed processing and large-scale data workloads. Drive performance improvements through effective partitioning, query optimization, resource management, caching, and tuning strategies. Establish standards for observability, monitoring, reliability, and operational excellence.

Collaborate with engineering teams throughout the software development lifecycle. Conduct architecture and design reviews for new features, data pipelines, and platform enhancements.

Promote adoption of CI/CD, automated testing, infrastructure-as-code, and DevOps best practices. Contribute to technical decision-making for platform enhancements and modernization initiatives.

Work closely with product managers, engineering leads, data scientists, and platform teams. Translate business requirements into scalable technical solutions and architecture designs. Communicate architecture decisions, technical trade-offs, and implementation approaches to stakeholders. Participate in roadmap discussions to ensure alignment between business goals and technology strategy. Collaborate across teams to drive successful implementation of data platform capabilities.

Mentor engineers on distributed systems design, Spark development, and data engineering best practices. Provide guidance through architecture reviews, design discussions, and technical workshops.

Required Knowledge And Experiences

Ensure architecture and design activities follow established SDLC and governance standards. Participate in code reviews, design reviews, and architecture assessments. Promote reliability, maintainability, scalability, and performance considerations throughout solution development. Drive continuous improvement in architecture practices and engineering standards.

Strong hands-on expertise with Apache Spark (Spark SQL, Structured Streaming, DataFrames, Dataset APIs). Experience designing and building large-scale distributed data processing systems. Strong knowledge of Spark optimization techniques including:

• Partitioning Strategies
• Shuffle Optimization
• Join Optimization
• Memory Management
• Resource Utilization
• Performance Tuning

Proficiency in Scala, Java, or Python. Experience with technologies such as:

• Hadoop
• Hive
• Iceberg
• AWS EMR
• AWS Glue
• GCP Dataproc
• BigQuery

Experience designing and operating batch and streaming data pipelines. Understanding of cloud-native architecture and distributed systems principles. Experience deploying Spark solutions on AWS and/or GCP platforms.

Individual contributor architecture role focused on distributed data platforms and Spark-based solutions. Provides architecture leadership and technical guidance across one or more engineering teams. Responsible for solution architecture quality, technology selection, design governance, and technical direction within the domain. Partners with Engineering Managers and Technical Leads to deliver scalable and reliable platform capabilities. Serves as a key technical advisor for Spark architecture, distributed systems design, and cloud-native data platforms.

• Experience with AWS services such as EMR, Glue, S3, Lambda, EKS, ECS, Step Functions, and CloudWatch.
• Experience with GCP services such as Dataproc, BigQuery, Cloud Storage, Dataflow, Pub/Sub, and Composer.
• Experience managing terabyte-to-petabyte scale data processing environments.
• Experience with Spark Structured Streaming, real-time analytics, and event-driven data processing.
• Contributions to Spark optimization, platform engineering, or open-source data ecosystem projects are a plus.

TransUnion Overview:

At TransUnion, we encourage and are committed to creating a real, positive impact and shared sense of purpose within our Workforce for Good, which empowers our people to grow, innovate and contribute to a better future for our communities and customers. We strive to build an environment where our associates are in the driver’s seat of their professional development— while having access to help along the way. We recognize that success comes when our associates thrive both professionally and personally; that’s why we prioritize work/life flexibility and offer resources for our teams across the globe to collaborate and drive excellence.

Be a part of our Workforce for Good – you’ll work with great people, pioneering products and cutting-edge technology.

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