TransUnion is a global leader in credit information, analytics, and data-driven decision‑making. With a presence in more than 30 countries, the company helps businesses and consumers make smarter financial choices by providing reliable data, sophisticated risk models, and actionable insights. In India, TransUnion has been expanding its technology footprint, focusing on building robust data pipelines, advanced analytics platforms, and innovative products that power credit scoring, fraud detection, and customer segmentation. The organization promotes a culture of continuous learning, encourages cross‑functional collaboration, and invests heavily in employee growth through mentorship programs, internal training, and exposure to cutting‑edge big‑data technologies. The role of Associate Data Engineer is designed for fresh graduates who are eager to dive into the world of big data engineering. Reporting to the Specialized Risk Group, the associate will assist in designing, building, and maintaining data ingestion pipelines that feed TransUnion’s core risk products. The first 90 days will be heavily oriented towards learning the existing Hadoop ecosystem, understanding data quality frameworks, and getting hands‑on with the company’s data stores. As confidence grows, the engineer will take ownership of small end‑to‑end features, contribute to algorithmic improvements, and collaborate with data scientists, product managers, and other engineering teams. Key responsibilities include: 1. Learn and operate the big‑data environment (Hadoop, HDFS, YARN) used at TransUnion. 2. Develop and maintain data ingestion jobs using Python, PySpark, or PyDoop. 3. Participate in data quality checks, validation, and monitoring of pipelines. 4. Assist in designing data models for NoSQL stores such as MongoDB, Cassandra, and Cloud Bigtable. 5. Support the implementation of entity‑linking algorithms to improve data accuracy. 6. Write Bash/Perl scripts for automation, log parsing, and routine maintenance tasks. 7. Collaborate with cross‑functional teams to gather requirements and translate them into technical specifications. 8. Document pipeline architecture, data lineage, and operational procedures. 9. Stay updated with emerging big‑data tools and propose improvements. 10. Contribute ideas during sprint planning and retrospectives to enhance product delivery. The tech stack revolves around Hadoop, Spark, Python, C/C++, NoSQL databases, and Linux‑based scripting. Growth prospects are strong; successful associates can progress to Data Engineer, Senior Data Engineer, and eventually to Lead or Architecture roles, with opportunities to specialize in machine‑learning pipelines or cloud data platforms. Joining TransUnion offers exposure to real‑world credit‑risk data, mentorship from seasoned data professionals, and a collaborative environment that values innovation and data integrity.