Join NTT DATA as a Junior Data Platform Modernization Engineer specializing in Databricks. This role is crucial for an enterprise data platform modernization initiative, focusing on migrating existing ETL workloads to Databricks.

We are looking for a proactive individual to contribute to building scalable and reliable data pipelines. You will leverage established migration patterns and common frameworks to ensure a smooth transition and enhanced data processing capabilities.

This is an opportunity to grow within a forward-thinking organization that values innovation and adaptability. Become part of a team dedicated to accelerating client success and making a positive societal impact.

Analyze existing Informatica PowerCenter and AWS Glue workloads to understand data mappings and transformation logic. Develop Databricks notebooks and data pipelines for migrating ETL processes to the Databricks Lakehouse. Implement data transformations using Python, PySpark, Spark SQL, and Databricks SQL, adhering to project architecture and coding standards. Develop and maintain batch and incremental processing pipelines utilizing Delta Lake and established control mechanisms. Support migration of AWS Glue jobs and database processing into Databricks, ensuring preservation of business logic. Perform unit testing, data reconciliation, and troubleshoot pipeline execution issues. Collaborate with senior developers and technical leads to resolve technical challenges and dependencies. Prepare technical documentation and facilitate knowledge transfer for migrated workloads. Assist with deployment, monitoring, and production stabilization activities.

Possess 3-6 years of experience in data engineering, ETL development, or application/data platform development. Demonstrate hands-on expertise with Databricks and Apache Spark, including strong development skills in Python/PySpark and SQL. Familiarity with Databricks notebooks, Spark SQL/Databricks SQL, Delta Lake, and Databricks Jobs/Workflows is essential. Experience developing batch or incremental ETL/ELT pipelines and working with relational databases is required. Understanding of data transformation, source-to-target mapping, and ETL development principles. Proficiency in unit testing, data validation, and troubleshooting issues. Basic knowledge of layered data architectures (e.g., Bronze/Silver/Gold) is beneficial. Ability to adhere to established development frameworks and coding standards is a must. A Bachelor's degree in Computer Science, Information Technology, Engineering, or equivalent practical experience is required.