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Common data-engineer title, mid-level scope, and broad Databricks/Spark/Azure skillset increases applicant competition.
Core data engineering skills (Spark, Databricks, ADF, Python) are highly transferable across industries.
Specific Databricks, Spark, ADF, Python, and Power BI requirements create moderate filtering without explicit years.
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Design, develop, and maintain data pipelines and ETL workflows using Databricks, Spark, and Azure Data Factory (ADF).
Implement and optimize data lakehouse architecture and data storage for performance and cost efficiency.
Develop and maintain Power BI dashboards, ensure data quality, security, and collaborate across teams to deliver high-quality data products.
Experience with data lakehouse architecture, Delta Lake, data modeling, and Azure cloud services.
Proficiency in Python programming for data processing and automation.
Familiarity with CI/CD pipelines, DevOps practices, and version control systems like Git.
Work Experience Required: Not explicitly mentioned in the JD.
Has hands-on experience building and optimizing complex cloud data pipelines and ETL workflows with Databricks, Spark, and Azure Data Factory.
Skilled in developing scalable data processing solutions and dashboards (Power BI) that provide actionable insights.
Comfortable working in cross-functional teams involving data scientists, analysts, and business stakeholders in a cloud-first environment.