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About the role
Role & responsibilities - Design, develop, and maintain scalable ETL/ELT pipelines using Databricks and PySpark. - Develop data processing solutions using Python and Spark for large-scale data transformation. - Write complex SQL queries for data extraction, transformation, validation, and reporting. Worked on Git repositories and CI/CD pipelines for deploying SQL files, DDL scripts, and Databricks notebooks across DEV, SIT, UAT, P01, and Production environments. . Supporting SIT, UAT, P01, and Production deployment activities. - Build and optimize data workflows to improve performance, scalability, and reliability. - Work with structured and semi-structured data from multiple data sources. - Collaborate with data analysts, business stakeholders, and development teams to understand data requirements. - Perform data quality checks, troubleshooting, and root cause analysis for data issues. - Optimize Spark jobs and SQL queries for improved performance. - Follow coding standards, documentation, and best practices for data engineering. - Participate in code reviews and support production deployments. Required Skills - Minimum 3 years of experience in Data Engineering. - Solid hands-on experience with Databricks. - Good experience in PySpark and Apache Spark. - Strong programming skills in Python. - Proficiency in SQL, including complex queries, joins, window functions, and performance tuning. - Experience in developing ETL/ELT pipelines. - Knowledge of data warehousing concepts and data modeling. - Experience with Git or other version control systems. - Strong analytical, problem-solving, and debugging skills. Preferred candidate profile .
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