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Mid-level metro role with popular Data Engineer title, partially offset by Databricks specialization.
High because role requires specialized Databricks, Delta Lake, and cloud data engineering expertise.
Mandates 5–8 years plus specific Azure Databricks, Unity Catalog, Spark, and Delta Lake expertise.
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Design, build, and maintain scalable ETL/ELT pipelines on Azure Databricks using medallion architecture (bronze to platinum layers).
Implement data governance and observability frameworks including Unity Catalog governance, audit tables, operational metadata, and data-quality monitoring.
Enable data sharing and migration across cloud platforms and curate AI/ML-ready datasets for analytics and reporting consumption.
Bachelor’s degree in IT, Computer Science, Data Science, Analytics, or Statistics required.
5–8 years of hands-on experience in data engineering with substantial experience in Azure Databricks.
Proficient in Spark/PySpark, SQL, Delta Lake medallion architecture pipelines, Unity Catalog governance, and data ingestion from various sources using streaming and connectors.
Experience with cloud data ecosystem migration/sharing (AWS, GCP, Snowflake, Redshift, BigQuery) into Azure Databricks lakehouse and data orchestration with CI/CD practices.
Experienced in implementing end-to-end data governance and data-quality frameworks with strong emphasis on pipeline observability and auditability.
Demonstrates expertise in building and optimizing Delta Lake data models for AI/ML and advanced analytics use cases.
Skilled in cross-cloud data movement and integration, with ability to support enterprise data governance standards and collaborative analytics environments.