Data Support Engineer (Databricks experience)

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).