Data Support Engineer (Databricks experience)
ARCHIVED
We can't find an active application page for this role right now. It may reopen or be listed elsewhere. Use Next Steps to search for an active apply link and similar live jobs.
Key ResponsibilitiesIncident & Problem Management: Provide L2/L3 support for data applications, resolving production issues and troubleshooting Databricks jobs, notebooks, and workflows.Performance Tuning: Optimize Spark applications, SQL queries, and Delta Lake tables to improve efficiency and reduce costs.Pipeline Maintenance: Monitor and troubleshoot ETL/ELT pipelines in Databricks (including Data Factory/Delta Live Tables), ensuring data quality and lineage (Unity Catalog).Collaboration: Act as a liaison between users, data engineering teams, and platform engineering, providing technical expertise and contributing to documentation.Automation: Create tools to automate routine support tasks and enhance support team productivity. Databricks +5Required Qualifications & SkillsTechnical Expertise: Strong hands-on experience with Apache Spark, Python/PySpark, and SQL.Databricks Ecosystem: Proficiency with Databricks Unified Data Analytics Platform, Delta Lake, and ideally Azure Databricks.Cloud Data Storage: Experience with Azure Data Lake Storage (ADLS Gen2) or similar data lake technologies.Version Control & CI/CD: Experience with Git/Azure DevOps for code management.Problem-Solving: Strong analytical skills, with the ability to diagnose complex data processing bottlenecks. "Nice-to-Have" SkillsData orchestration tools (e.g., Apache Airflow, Azure Data Factory).Data governance tools (e.g., Unity Catalog, Collibra).Streaming data knowledge (e.g., Spark Structured Streaming).