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Mid-level generalist data engineer with common skills and experience band increases applicant competition.
Core data engineering skills transfer across industries, though mandatory Databricks raises specialization slightly.
Mandatory Databricks, PySpark, and explicit 4–8 years requirement create strict filters.
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Develop, maintain, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, Python, and SQL.
Integrate data from multiple sources into data lakes and data warehouses ensuring data quality and performance.
Collaborate across teams and use Git, CI/CD, and deployment best practices to deliver robust data platform solutions on cloud platforms like Azure, AWS, or GCP.
4–8 years of experience in Data Engineering / ETL work.
Strong hands-on experience with Databricks is mandatory.
Proficient in PySpark, Python, and SQL for data processing and transformation.
Experience with cloud platforms such as Azure, AWS, or GCP.
Experienced in building and operating ETL pipelines on Databricks within cloud environments, especially Azure.
Familiar with data lakes, data warehouses, and data integration architectures.
Comfortable working with CI/CD practices and cross-functional teams including data architects and business stakeholders.