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Mid-level, popular data-engineer title in Bengaluru with broad Databricks/PySpark requirements yields high competition.
Core data-engineering skills are transferable but regulated life-sciences domain favors industry experience.
Explicit 4–7 years and mandatory Databricks, PySpark, Python, SQL, and CI/CD requirements increase shortlisting strictness.
Lead design and development of scalable ETL pipelines for the Enterprise Data Lakehouse using Databricks, PySpark, SQL, and Python.
Develop, optimize, and maintain data pipelines and processing solutions including incremental data processing and performance improvements.
Manage source code and implement CI/CD pipelines using GitHub, ensuring efficient version control and deployment workflows.
Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field.
4-7 years of relevant experience in Data Engineering, ETL, Data Warehousing, or data analytics decision support roles.
Strong hands-on experience with Databricks platform, PySpark, Python, SQL, and ETL tools like SSIS and Azure Data Factory.
Experience with version control systems (Git) and CI/CD pipeline implementation; knowledge of data warehousing concepts and Big Data technologies (Spark, Hadoop, Kafka).
Experienced in managing end-to-end data pipeline development in cloud-based environments, particularly using Azure Databricks.
Technically proficient in both development and operational aspects including source code management, performance tuning, and CI/CD.
Skilled at translating business requirements into scalable technical solutions following standard data engineering practices and methodologies.