Data Engineer

Job description

TransUnion's Job Applicant Privacy Notice Team Overview The Argus Data Engineering Team, part of the Global Technology (GT) organization, is responsible for designing, developing, maintaining, and supporting enterprise-grade data pipelines and data products that power critical business functions across the Argus organization. The team plays a key role in enabling data-driven decision making by delivering scalable, reliable, and high-quality data solutions that support teams such as Enterprise Data Management (EDM), Account Performance Management (APM), Data Science, Analytics, and other business stakeholders.

This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.Role Overview And Core Responsibilities This role exists to support the growing demand for scalable, secure, and efficient data engineering solutions across the Argus organization. As data continues to be a strategic asset, this position is critical in building and maintaining data platforms and pipelines that enable analytics, reporting, machine learning, and business intelligence initiatives. Business Outcomes Driven by this Role • Deliver reliable and scalable data pipelines that support business-critical reporting, analytics, and data science initiatives. • Improve data availability, accuracy, and quality across multiple Argus platforms and products. • Enable faster time-to-insight for business and technology stakeholders through efficient data processing and delivery. • Support cloud modernization and migration efforts by leveraging AWS and GCP cloud-native technologies. • Reduce operational overhead through automation, monitoring, and optimization of data workflows. Core Responsibilities • Design, develop, and maintain robust data pipelines using Python, SQL, and cloud-native technologies. • Build and support data engineering solutions for cross-functional teams including EDM, APM, Data Science, and Analytics. • Develop scalable ETL/ELT processes to ingest, transform, and deliver data from multiple internal and external sources. • Create and manage workflow orchestration processes using Apache Airflow to ensure reliable and automated data movement. • Monitor pipeline performance, troubleshoot issues, and implement improvements to ensure data integrity and system reliability. • Collaborate with business stakeholders, data scientists, analysts, and engineering teams to understand data requirements and deliver effective solutions. • Implement best practices for data governance, security, performance optimization, and operational excellence. • Participate in code reviews, testing, deployment, and ongoing support activities for production data platforms. • Contribute to cloud-based architecture design and continuous improvement initiatives across AWS and GCP environments. Support Agile development processes and actively participate in sprint planning, estimation, and delivery activities Required Knowledge And Experiences • 3-4 years of Data Engineering experience building and supporting enterprise-scale data pipelines and data integration solutions.• Why it matters: Enables the individual to independently design, implement, and troubleshoot complex data workflows. • Strong Python and SQL expertise• Why it matters: Python is used for pipeline development, automation, and data transformation, while SQL is essential for data modeling, querying, validation, and optimization. • Experience with Apache Airflow• Why it matters: Airflow is the primary orchestration platform used to schedule, monitor, and manage data workflows across the organization. • Multi-cloud experience with AWS (Amazon Web Services) and GCP (Google Cloud Platform)• Why it matters: Argus data products are deployed across cloud environments, requiring familiarity with cloud-native services, storage, compute, and data processing technologies. • Experience with data warehousing, ETL/ELT frameworks, and large-scale data processing Why it matters: Ensures efficient data movement, transformation, and delivery for analytical and operational use cases • Education• Bachelor's degree in Computer Science, Computer Engineering, Information Systems, or a related technical field, or equivalent practical experience. • Why it matters: Provides a strong foundation in software engineering, database concepts, algorithms, and system design. Required Technical Skills • 3-4 years of hands-on experience in Data Engineering. • Strong proficiency in Python for data pipeline development, automation, and transformation. • Advanced SQL (Structured Query Language) skills for data extraction, transformation, optimization, and analysis. • Experience with Apache Airflow for workflow orchestration and scheduling. • Hands-on experience with AWS (Amazon Web Services) and GCP (Google Cloud Platform) cloud-native services. • Understanding of ETL/ELT frameworks, data warehousing concepts, and modern data architecture patterns. • Experience working with source control systems (Git) and Agile software development methodologies. Preferred Skills While not required, the following skills would accelerate success