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Databricks specialization but common Data Engineer title and metro location create moderate competition.
Databricks-focused data engineering skills are specialized and less transferable across industries.
Explicit 5+ years and required Databricks, PySpark, Python, and AWS skills impose strict filters.
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Design, develop, and maintain scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Integrate and manage data from multiple sources including databases, Amazon S3, and REST APIs leveraging Databricks Lakehouse features such as Unity Catalog and Delta Lake with Medallion Architecture.
Optimize Spark workloads, implement data quality validations, and collaborate across teams to deliver reliable production-ready data solutions.
Strong expertise in Python, PySpark, and advanced SQL.
Hands-on experience with Databricks Lakehouse Platform including Unity Catalog and Delta Lake.
Work Experience Required: 5+ years in Data Engineering with at least 2 years hands-on with Databricks.
Experience integrating data via REST APIs and managing structured and semi-structured data formats (CSV, JSON, Parquet, Delta).
Experienced in building and optimizing Spark-based ETL/ELT pipelines in enterprise environments with large volumes of data.
Familiarity with data modeling concepts such as Star Schema, Snowflake Schema, and slowly changing dimensions (SCD).
Comfortable working with Databricks platform features including Jobs, Workflows, Clusters, and Medallion Architecture; ideally with additional exposure to Airflow, Kafka, or dbt.