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Specialized Databricks lead role but common data-engineer title increases applicant density.
Requires Databricks, Spark, and data-warehousing expertise, limiting cross-industry transferability.
Explicit 9+ years, required Databricks/PySpark expertise, and lead experience create stringent filters.
Design, develop, and maintain scalable ETL/ELT data pipelines using Databricks Lakehouse platform with PySpark and SQL.
Integrate data from multiple sources including REST APIs, databases, and cloud storage (AWS S3) while managing Delta Lake tables using Medallion Architecture.
Optimize Spark workloads and Databricks Jobs/Workflows for performance, reliability, and scalability; ensure data quality through validations and monitoring.
9+ years of experience in data engineering, including 3+ years hands-on with Databricks.
Strong expertise in Python, PySpark, Advanced SQL, and Databricks features including Unity Catalog, Delta Lake, and Medallion Architecture.
Experience with ETL/ELT pipeline development, REST API integrations, data modeling (Star/Snowflake schema), and Spark performance tuning.
Work Experience Required: 9+ years in Data Engineering, Prior Lead/Technical Lead experience preferred.
Experienced technical lead with proven ability to guide data engineering teams and drive architecture/technical decisions.
Deep knowledge of Databricks ecosystem and hands-on experience optimizing data pipelines in large-scale production environments.
Familiarity with modern data ingestion and workflow orchestration tools such as Auto Loader, Kafka, Airflow, or dbt is advantageous but not mandatory.