Key Responsibilities:

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  Design, build, and maintain scalable data pipelines and ETL processes.

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  Work with large datasets to ensure high data quality, accuracy, and performance.

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  Develop data solutions leveraging Azure Databricks, PySpark, and SQL.

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  Collaborate with cross-functional teams including Data Scientists, Analysts, and Product teams to support data-driven initiatives.

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  Troubleshoot and optimize data workflows to improve reliability and efficiency.

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  Ensure compliance with best practices in data governance, security, and performance.

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  Contribute to agile ceremonies and work in a collaborative, fast-paced environment.

Required Skills & Qualifications:

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  7–8 years of experience in Data Engineering.

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  Strong programming skills in Python and SQL.

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  Hands-on experience with PySpark for large-scale data processing.

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  Good working knowledge of Azure Databricks.

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  Strong problem-solving and debugging skills.

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  Experience in designing and optimizing ETL pipelines.

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  Knowledge of Agile methodologies (Scrum/Kanban) is a plus.

Nice to Have:

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  Familiarity with other Azure services such as Data Lake, Synapse, or Data Factory.

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  Exposure to CI/CD practices and version control systems (e.g., Git).

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  Experience working in a cloud-first data ecosystem.