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Strong employer brand, metro Bangalore, mid-level generalist Databricks/PySpark role attracts many qualified applicants.
Skills are cloud and tools-focused and highly transferable across industries.
Multiple mandatory technologies plus explicit 2–5 years experience and Databricks/PySpark requirements.
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Design, develop, and maintain scalable data pipelines primarily using Python and PySpark on Azure Databricks.
Optimize and manage large-scale data warehousing solutions including Delta Lake, Databricks cluster configurations, and query tuning.
Work with orchestration tools like Apache Airflow and collaborate cross-functionally to ensure data reliability and governance.
2 to 5 years of professional experience in data engineering or related roles.
Advanced proficiency in Python and strong hands-on experience with PySpark.
Deep expertise in Azure Databricks, including Delta Lake, cluster management, and SQL for analytics and data warehousing.
Experience with cloud data warehousing platforms such as Snowflake, Azure Synapse Analytics, or AWS Redshift.
Experienced in operating within cloud environments focusing on Azure-based big data services and platforms.
Skilled in designing efficient, scalable data architectures and optimizing data pipelines for performance and reliability.
Ability to integrate DevOps practices (e.g., CI/CD, Docker, Kubernetes) into data engineering workflows is an advantage but not mandatory.