About the role
- Develop and maintain ETL/ELT data pipelines using Databricks.
- Work with PySpark, Python, and SQL for data processing and transformation.
- Integrate data from multiple sources into data lakes and data warehouses.
- Develop, optimize, and monitor data pipelines.
- Implement data quality checks, error handling, and performance optimization.
- Work with cloud platforms such as Azure, AWS, or GCP.
- Collaborate with data engineers, architects, and business teams.
- Follow Git, CI/CD, and deployment best practices.
- 4–8 years of experience in Data Engineering / ETL.
- Strong hands-on experience with Databricks – Mandatory.
- Good experience in PySpark, Python, and SQL.
- Strong knowledge of ETL/ELT concepts and data pipelines.
- Experience with Data Lake / Data Warehouse.
- Experience with Azure, AWS, or GCP.
- Good understanding of data integration, data modeling, and data quality.
- Experience with Azure Data Factory / AWS Glue.
- Knowledge of Delta Lake.
- Experience with Apache Spark / Kafka.
- Experience with Airflow.
- Knowledge of CI/CD and DevOps.