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Strong employer brand, mid-level generalist title, and broad cloud/Databricks skillset increase applicant competition.
Core cloud data engineering skills (SQL, Spark, Python, ETL) are highly transferable across industries.
Mandatory Azure/Databricks/Delta Lake/PySpark and pipeline experience creates strict technical filtering for shortlisting.
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Build, maintain, and optimize data ingestion pipelines and notebooks on Azure and Databricks using Delta Lake and PySpark to support reliable data delivery.
Collaborate with architects, leads, product owners, and operations team to troubleshoot issues, provide technical guidance, and deliver practical data solutions across a cloud-native data platform.
Contribute to platform evolution by applying new Azure and Databricks capabilities, using CI/CD (Azure DevOps) practices, and enabling analytics via Power BI reporting.
Strong fundamentals in SQL, coding, and problem-solving skills.
Hands-on experience with building or supporting data pipelines or components in cloud or distributed data environments.
Experience or willingness to work deeply with Azure, Databricks, Delta Lake, and PySpark.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced with cloud data engineering tools and collaborative software development practices (version control, code review) in cross-functional teams.
Demonstrates ability to work effectively across global teams and time zones with clear communication and proactive troubleshooting.
Familiarity or interest in Agile delivery, CI/CD with Azure DevOps, Power BI reporting, and experimentation with AI-assisted development tools and emerging platform features.