TransUnion

Sr. Consultant - Data Engineering

In short

This role designs, builds, and maintains data pipelines and analytical platforms supporting fraud detection, monitoring, and investigation within TransUnion's Fraud Prevention organization. It involves partnering with data scientists to operationalize fraud models, building ETL/ELT workflows, and supporting real-time streaming architectures for fraud decisioning. The position requires 2 years of data engineering experience, strong SQL and Python/Scala skills, hands-on Spark/PySpark experience, and expertise in the Google Cloud Platform ecosystem. It is a remote position that may occasionally require in-person attendance at work events.

Team Overview
At TransUnion, we are committed to using information for good. We foster an environment where innovation, professional growth, and collaboration drive meaningful outcomes for our customers and communities. As part of our Fraud Prevention organization, you will work with talented data scientists, engineers, product leaders, and fraud experts to build and scale data platforms that support advanced fraud analytics, machine learning, and real-time decisioning solutions.
You will work alongside data scientists, fraud strategists, software engineers, and product leaders to develop high-performance data environments that power some of the industry's leading fraud prevention capabilities.
This is a remote position which may require occasional in-person attendance at work-related events at the discretion of management.
Role Overview and Core Responsibilities
As a Data Engineer supporting Fraud Solutions, you will be responsible for designing, developing, and maintaining robust data pipelines, data products, and analytical platforms that enable fraud detection, monitoring, and investigation.
Key responsibilities include:
•Lead the global technical development, deployment, and maintenance of Cyber Defense data ecosystem, using the latest data programming languages and visualization tools. This includes optimizing 'big data' data pipelines, architectures and data sets to enhance anomaly detection tools
•Design, build, and optimize scalable batch and real-time data pipelines supporting fraud detection and risk analytics.
•Partner with Data Scientists to operationalize fraud models and analytical solutions in production environments.
•Build and manage ETL/ELT workflows for ingestion and transformation of structured and unstructured data.
•Design, develop and publish charts and dashboards for alerts on potential fraud and regulatory violations.
•Ensure data quality, governance, lineage, observability, and monitoring across fraud data ecosystems.
•Develop reusable frameworks and automation tools that accelerate fraud analytics and model deployment.
•Collaborate with product, engineering, cybersecurity, and fraud teams to translate business requirements into scalable technical solutions.
•Support streaming architectures that enable near real-time fraud detection and decisioning.
•Optimize platform performance, reliability, and cost efficiency across cloud environments.
•Troubleshoot complex data and pipeline issues while ensuring high availability and operational excellence.
•Document architecture, technical standards, and engineering best practices.
•Design and implement solutions to automate department tools and technology to drive more efficiency and productivity.
Required Knowledge and Experiences
•Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Engineering, or related technical discipline.
•2 years of experience in data engineering, software engineering, or large-scale data platform development.
•Demonstrated expertise in performing data engineering work within Google Cloud ecosystem (GCP), designing and developing dashboards in visualization tools.
•Good understanding of data warehousing concepts and technologies.
•Strong SQL expertise and advanced proficiency in Python or Scala.
•Experience designing and maintaining enterprise-scale ETL/ELT pipelines.
•Hands-on experience with distributed data processing frameworks such as Spark/PySpark.
•Experience working with large-scale datasets and cloud-native data platforms.
•Strong understanding of data architecture, dimensional modeling, and advanced database concepts.
•Experience implementing data quality controls, reconciliation processes, and monitoring frameworks.
•Knowledge of software engineering best practices including CI/CD, automated testing, source control, and DevOps principles.
•Understanding of the business intelligence development life cycle including the ability to lead collaboration discussions to gather the needs for desired outcomes for the BI solutions being built.
•Strong communication and stakeholder management skills.
TransUnion Job Title: Sr Consultant, Data Integration

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