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Mid-level Databricks data engineer with common skills and metro location increases qualified applicant density.
Databricks specialization moderately limits transferability, though core ETL and SQL skills remain transferable.
Explicit 4+ years plus mandatory Databricks, PySpark, SQL and ETL skills enforce strict filtering.
Design, develop, and maintain scalable ETL/ELT data pipelines on the Databricks Lakehouse using PySpark, SQL, and Python.
Integrate and process data from multiple sources including databases, Amazon S3, and REST APIs, implementing business logic and data transformations.
Manage and optimize Databricks Jobs, Delta Lake tables following Medallion Architecture, ensuring data quality, performance, and scalability.
4+ years of experience in Data Engineering with at least 2 years hands-on experience on Databricks.
Strong expertise in Python, PySpark, Advanced SQL, and ETL/ELT development.
Experience with Databricks Lakehouse Platform components including Unity Catalog, Delta Lake, Workflows/Jobs, and Medallion Architecture.
Experience integrating with REST APIs and working knowledge of data warehousing concepts and data modeling (Star/Snowflake schemas, SCD).
Experienced in building production-ready data pipelines in cloud environments, specifically leveraging Databricks and AWS.
Capable of optimizing Spark workloads for performance and reliability and managing complex data transformation and modeling.
Familiarity with version control and CI/CD processes, with possible additional skills in Kafka, Airflow, dbt, or Databricks certification seen as advantages.