{"schemaVersion":"jobsearcher.job.v1","id":"808eaa6877804f9da36f424d","url":"https://jobsearcher.com/jobs/808eaa6877804f9da36f424d","canonicalUrl":"https://jobsearcher.com/jobs/808eaa6877804f9da36f424d","title":"Data Bricks Migration","description":"Role: Data Bricks Migration and Support engineer Location: Seattle, WA / Bellevue, WA / Everett, WA / Renton, WA / Richardson, TX / Plano, TX / Dallas, TX / St. Louis, MO / Charleston, SC / Arlington, VAResponsibilitiesSupport post-migration environment from IBM DataStage to DatabricksIncident & Lifecycle ManagementCI/CD Deployment: Support code deployments across Development, Test, and Production environments using Databricks Repos and REST APIsMonitoring & Alerting: Set up monitoring via Databricks System Tables and observability tools to catch job failures, data anomalies, or latency spikes earlyPipeline Maintenance & OrchestrationWorkflow Management: Transition from DataStage job sequences to native data bricks workflows for scheduling, dependency tracking, and alertsETL Refactoring: Troubleshoot and fix issues in generated PySpark or Spark SQL code that replaced legacy DataStage Transformer or Lookup stagesStreaming & Batch Integration: Support ongoing data ingestion using data bricks autoloader to process files continuously from cloud storagePerformance Tuning & Cost OptimizationCompute Management: Monitor and configure serverless or classic clusters to prevent over-provisioningQuery Optimization: Analyze Spark execution plans. Replace inefficient row-by-row processing logic (a common DataStage carryover) with vectorized operations and native Spark functionsStorage Optimization: Maintain Delta Lake tables by enforcing layout optimization (\\(ZORDER\\)Data Governance & SecurityAccess Control: Implement granular permissions, column-masking, and row-level filters using Data bricks unity catalog to replace DataStage's legacy security policiesData Quality: Utilize Delta Live Tables (DLT) to build pipelines with built-in, declarative data quality expectations and monitoring","company":"Envision Technology Solutions","rawCompany":"envision technology solutions","city":"Seattle","state":"WA","isRemote":false,"isActive":false,"createdAt":"2026-07-27T11:48:22.172Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Bricks Migration","description":"Role: Data Bricks Migration and Support engineer Location: Seattle, WA / Bellevue, WA / Everett, WA / Renton, WA / Richardson, TX / Plano, TX / Dallas, TX / St. Louis, MO / Charleston, SC / Arlington, VAResponsibilitiesSupport post-migration environment from IBM DataStage to DatabricksIncident & Lifecycle ManagementCI/CD Deployment: Support code deployments across Development, Test, and Production environments using Databricks Repos and REST APIsMonitoring & Alerting: Set up monitoring via Databricks System Tables and observability tools to catch job failures, data anomalies, or latency spikes earlyPipeline Maintenance & OrchestrationWorkflow Management: Transition from DataStage job sequences to native data bricks workflows for scheduling, dependency tracking, and alertsETL Refactoring: Troubleshoot and fix issues in generated PySpark or Spark SQL code that replaced legacy DataStage Transformer or Lookup stagesStreaming & Batch Integration: Support ongoing data ingestion using data bricks autoloader to process files continuously from cloud storagePerformance Tuning & Cost OptimizationCompute Management: Monitor and configure serverless or classic clusters to prevent over-provisioningQuery Optimization: Analyze Spark execution plans. 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