Data Engineer - Databricks

We are looking for an experienced Databricks Data Engineer to support, maintain, and enhance existing Databricks-based data applications and pipelines. The role focuses on ensuring reliability, performance, and scalability of production Databricks workloads rather than building new platforms from scratch. You will work closely with data, analytics, and engineering teams to keep critical data applications stable, optimized, and aligned with business needs.

Responsibilities:

• Support and maintain existing Databricks applications, notebooks, jobs, and Delta Lake pipelines in production.
• Monitor, troubleshoot, and resolve issues related to job failures, performance degradation, data quality, and cluster utilization.
• Optimize existing Spark jobs, SQL queries, and Delta tables for cost, performance, and reliability.
• Manage and improve Databricks workspace configurations, including clusters, job scheduling, access controls, and Unity Catalog (where applicable).
• Implement and maintain data quality checks, logging, alerting, and basic observability for Databricks workloads.
• Collaborate with stakeholders to understand requirements for enhancements or bug fixes on existing applications.
• Perform incremental improvements, refactoring, and technical debt reduction on current Databricks solutions.
• Ensure adherence to best practices around security, governance, and cost management within the Databricks environment.
• Document existing pipelines, dependencies, and operational runbooks.
• Participate in on-call or support rotations as needed to maintain production stability

Requirements:

• 6-9 years of overall experience in data engineering, with strong hands-on experience in Databricks.
• Solid proficiency in Apache Spark (PySpark and/or Scala) and SQL.
• Proven experience supporting and optimizing production Databricks workloads (jobs, notebooks, Delta Lake, workflows).
• Strong understanding of Delta Lake concepts (ACID transactions, time travel, and optimization techniques such as Z-ordering, vacuum, and optimization).
• Experience with Databricks Job clusters, interactive clusters, and performance tuning (partitioning, caching, shuffle optimization, and autoscaling).
• Familiarity with data modeling, ETL/ELT patterns, and production data pipeline support.
• Experience working with cloud platforms (preferably Azure, AWS, or GCP) in the context of Databricks.
• Ability to troubleshoot complex Spark and Databricks issues independently.
• Strong communication skills and ability to work effectively with an EST-aligned team.

Preferred Qualifications:

• Experience with Unity Catalog, Databricks SQL, or Lakehouse architecture.
• Knowledge of CI/CD practices for Databricks (e. g., Databricks Asset Bundles, Git integration, Terraform/ARM templates).
• Familiarity with orchestration tools (Airflow, Azure Data Factory, or Databricks Workflows).
• Exposure to data quality frameworks, monitoring tools, or cost optimization initiatives on Databricks.
• Experience supporting analytics or BI teams consuming Databricks data products.