We are looking for a Senior Databricks Engineer with 7+ years of experience in data engineering and strong hands-on expertise in Databricks and Apache Spark. The ideal candidate will design and build scalable data pipelines, optimize large-scale data processing, and work with cloud-based data platforms.

Key Responsibilities

Design, develop, and maintain scalable data pipelines using Databricks and Apache Spark.
Develop and optimize PySpark/Spark jobs for large-scale data processing.
Build reliable ETL/ELT pipelines using Databricks workflows, Delta Lake, and SQL.
Implement data transformations, data quality checks, and performance optimization.
Work with cloud data platforms such as AWS, Azure, or GCP.
Optimize Spark jobs, cluster configurations, partitioning, joins, and data processing performance.
Collaborate with data engineers, data scientists, analysts, and business teams to deliver data solutions.
Implement best practices for CI/CD, version control, monitoring, and production support.
Troubleshoot data pipeline and performance issues in production environments.

Required Skills

7+ years of experience in Data Engineering.
Strong hands-on experience with Databricks.
Strong expertise in Apache Spark / PySpark.
Experience with Delta Lake, Databricks Workflows, and Spark SQL.
Strong proficiency in Python and SQL.
Experience building and optimizing large-scale ETL/ELT pipelines.
Experience with at least one cloud platform: AWS, Azure, or GCP.
Good understanding of data warehousing, data modeling, and distributed data processing.
Experience with Git and CI/CD practices.

Preferred Skills

Experience with Unity Catalog and Databricks governance.
Experience with Azure Data Factory, AWS Glue, or similar orchestration tools.
Knowledge of streaming technologies such as Kafka or Spark Structured Streaming.
Experience with Infrastructure as Code or cloud automation.
Databricks certifications are a plus.

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