**Roles & Responsibilities**

+ Develop, test, and maintain data pipelines using Databricks, PySpark, and Python.
+ Ingest, transform, and process structured and semi-structured data from multiple sources.
+ Support the development of scalable ETL/ELT workflows for analytics, reporting, and machine learning use cases.
+ Work with data engineers, analysts, and data scientists to understand data requirements and deliver reliable datasets.
+ Perform data cleansing, validation, and quality checks to ensure accuracy and consistency.
+ Optimize Spark jobs and Databricks notebooks for performance, reliability, and cost efficiency.
+ Create and maintain documentation for data pipelines, workflows, data definitions, and processes.
+ Assist in troubleshooting pipeline failures, data issues, and performance bottlenecks.
+ Follow best practices for version control, code quality, testing, and deployment.
+ Support basic AI/ML data preparation activities, including...