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Mid-level, popular data role in metros with broad skills raises competition.
Core data engineering skills (Spark, Databricks, Python, ADF) are highly transferable across industries.
Multiple mandatory technologies (Databricks, Spark, ADF, Delta Lake, Power BI) increase screening rigor.
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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 for performance and cost efficiency, including data storage and retrieval.
Develop and maintain Power BI dashboards/reports and ensure data quality, security, and governance in all data solutions.
Experience with Databricks, Spark, Azure Data Factory, and Python programming for data processing.
Knowledge of data lakehouse architecture, Delta Lake, data modeling, CI/CD pipelines, DevOps practices, and version control (Git).
Work Experience Required: Not explicitly mentioned in the JD.
Employment offers conditional upon screening criteria passage.
Experienced in cloud data engineering focused on scalable and efficient data pipelines with strong Azure ecosystem skills.
Proficient in implementing data lakehouse architectures and cost/performance optimization strategies.
Capable of collaborating across data science, analytics, and business teams to deliver reliable, high-quality data products.