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Strong employer brand, metro location, mid-level generalist data role with broad cloud/Databricks skills increases competition.
Core data engineering skills (Python, PySpark, ETL, SQL, cloud) are highly transferable across industries.
Explicit 2–4 years requirement plus mandatory Databricks, PySpark, and cloud/data warehousing skills makes shortlisting strict.
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Design, build, and maintain large-scale data pipelines and ETL/ELT workflows using Python and PySpark on Azure Databricks.
Implement and manage Delta Lake and Databricks cluster configurations to optimize data warehouse performance.
Use orchestration tools like Apache Airflow for scheduling and workflow management while ensuring data reliability and governance.
2 to 4 years of relevant experience in data engineering.
Advanced proficiency in Python and strong hands-on experience with PySpark.
Deep expertise in Azure Databricks including Delta Lake and cluster management.
Strong SQL skills and experience with data warehousing platforms like Snowflake, Azure Synapse Analytics, or AWS Redshift.
Experienced in cloud-based data engineering particularly on Azure ecosystem with strong focus on Azure Databricks.
Familiarity with DevOps practices related to CI/CD pipelines and containerization (Docker, Kubernetes) is preferred.
Able to work in a collaborative, cross-functional environment focused on delivering scalable data solutions.