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Metro location, common data-engineer title, and broad candidate pool offset by Databricks specialization.
Core data engineering skills transferable, but Databricks/PySpark requirement raises domain specificity.
Mandatory Databricks, PySpark, SQL, and data modeling requirements create strict technical screening.
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Design, build, and maintain scalable data pipelines and curated data assets on Databricks, ensuring performance optimization and reliability.
Develop ETL/ELT processes using SQL, Python, PySpark, and Dbt for ingestion, transformation, validation, and publishing of multi-source data.
Create reusable, well-documented pipelines and build BI-ready datasets, data marts, and reporting tables for analytics platforms like Power BI and Sigma Computing.
Strong hands-on experience with Databricks data engineering and scalable pipeline development.
Proficiency in SQL, Python, and PySpark mandatory.
Experience designing and developing ETL/ELT processes from multiple data sources.
Work Experience Required: Not explicitly mentioned in the JD.
Demonstrated expertise in data engineering with focus on BI enablement and analytics delivery in enterprise environments.
Strong understanding of data modeling concepts including fact/dimension tables, star schemas, and semantic layers.
Experienced in data pipeline monitoring, data quality validation, reconciliation, and troubleshooting in hybrid workplace settings.