DataBricks Data Engineer USA Remote (United States)

S27a; Northern (KY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

S27a is seeking an experienced Databricks Data Engineer to maintain and optimize production data pipelines and Delta Lake workloads. You will ensure reliability, performance, and cost efficiency while collaborating with data, analytics, and engineering teams.

The role emphasizes production support, cluster tuning, and observability, with opportunities to implement best practices across security and governance in a cloud environment.

Qualifications

• 6–9 years of data engineering experience with Databricks.
• Strong Spark (PySpark/Scala) and SQL skills.
• Proven production Databricks workloads experience.
• Deep Delta Lake knowledge: ACID, time travel, optimization.
• Experience with Databricks clusters and performance tuning.
• Cloud experience with Azure, AWS or GCP in Databricks context.
• Ability to troubleshoot complex Spark/Databricks issues independently.

Responsibilities

• Support and maintain existing Databricks apps, notebooks, and Delta Lake pipelines in production.
• Monitor, troubleshoot, and resolve job failures and performance issues.
• Optimize Spark jobs, SQL queries, and Delta tables for cost and performance.
• Manage workspace configurations, clusters, job scheduling, Unity Catalog.
• Implement data quality checks, logging, and observability.
• Collaborate with stakeholders on enhancements or bug fixes.
• Perform incremental improvements and reduce technical debt.
• Ensure security, governance, and cost management in Databricks.
• Document pipelines, dependencies, and runbooks.
• Participate in on-call rotations.
• Finance Data Engineering Support.

Skills

Databricks

Apache Spark

SQL

Delta Lake

Performance tuning

Cloud platforms

Troubleshooting

Data pipelines

Tools

Airflow

Azure Data Factory

Databricks Workflows

Job description

Role Overview

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 net-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.

Key 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
• Finance Data Engineering Support

Required Qualifications

• 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, optimization techniques such as Z‑ordering, vacuum, optimize).
• Experience with Databricks Job clusters, Interactive clusters, and performance tuning (partitioning, caching, shuffle optimization, 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.

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.
• Collaborative remote environment with regular syncs and support responsibilities