Join NTT DATA as a Junior Data Platform Modernization Engineer, specializing in Databricks. This role is integral to an enterprise data platform modernization initiative, focusing on migrating legacy systems to Databricks.

Ideal candidates are hands-on data engineers with robust PySpark, Python, and SQL skills, eager to contribute to scalable and reliable data pipeline development. You will work with established migration patterns and frameworks, playing a key role in transforming existing ETL workloads.

Your day-to-day will involve analyzing Informatica and AWS Glue workflows to understand data mappings and transformations. You'll develop Databricks notebooks and pipelines to migrate ETL workloads to the Databricks Lakehouse, implementing data transformations using Python, PySpark, and SQL.

Key responsibilities include developing Bronze, Silver, and Gold layers, creating batch and incremental processing pipelines with Delta Lake, and managing Databricks Jobs/Workflows. You'll also convert Informatica transformations, support AWS Glue job migration, and perform unit testing and data reconciliation.

Additional duties encompass troubleshooting data and pipeline issues, supporting integration and UAT testing, collaborating with senior developers, adhering to coding standards, participating in code reviews, and preparing technical documentation. You will also assist with deployment and production stabilization.

We require 3–6 years of data engineering experience, with hands-on expertise in Databricks and Apache Spark. Strong development skills in Python/PySpark and SQL are essential.

Experience with Databricks notebooks, Spark SQL/Databricks SQL, Delta Lake, and Databricks Jobs/Workflows is necessary. You should have experience developing batch or incremental ETL/ELT pipelines and familiarity with ETL tools like Informatica PowerCenter or AWS Glue.

Understanding of data transformation, source-to-target mapping, and ETL development is crucial. We are looking for individuals with experience in unit testing, data validation, troubleshooting, and a basic grasp of layered data architectures. A Bachelor's degree in Computer Science, Information Technology, Engineering, or equivalent experience is required, along with good analytical, problem-solving, and communication skills.