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We are looking for an experienced Senior Databricks Engineer to support enterprise data initiatives. This Long-term Contract position will focus on building and enhancing scalable data solutions, including extracting data from ServiceNow and structuring it for downstream ARDoc consumption within a broader enterprise architecture program. The role will partner with engineering, analytics, and platform teams to deliver reliable, governed, and high-performing data pipelines on Databricks.
Job Responsibility
Develop and maintain robust data pipelines in Databricks using Apache Spark to support enterprise reporting and analytical use cases
Extract and transform data from ServiceNow, then create and manage structured tables that supply ARDoc as part of a larger architecture initiative
Design layered data models using Delta Lake practices to improve scalability, data usability, and long-term maintainability
Configure and manage Databricks jobs, workflows, and compute resources to improve processing efficiency and platform stability
Investigate performance issues across Spark workloads and implement tuning strategies that improve speed, reliability, and cost control
Establish data governance practices through cataloging, lineage visibility, and secure access controls within the Databricks environment
Work closely with data scientists, analysts, and platform teams to deploy production-ready data assets and support advanced analytics needs
Provide technical guidance to other engineers by promoting engineering standards, reviewing designs, and sharing platform best practices
Requirements
At least 5 years of experience in data engineering working with large-scale or distributed data processing platforms
Minimum 3 years of hands-on experience building solutions in Databricks within production environments
Strong coding ability in Python, especially PySpark, with additional SQL proficiency for data transformation and analysis
Solid knowledge of Apache Spark concepts, including execution behavior, performance tuning, and resource management
Practical experience with Delta Lake features such as schema evolution, versioned data management, and reliable transactional processing
Familiarity with cloud-based data ecosystems and orchestration tools used to schedule, monitor, and manage data workloads
Experience using version control and CI/CD practices to support repeatable and controlled data engineering deployments
ServiceNow knowledge is highly valued, especially for roles involving data extraction and integration into downstream platforms
Nice to have
ServiceNow knowledge is highly valued, especially for roles involving data extraction and integration into downstream platforms