Databricks Data Engineer

Databricks Data EngineerJob Location: Seattle, WA (Onsite) Job Type: Long Term Contract Pay Range: $50 to $60/Hr on W2Must Have Technical/Functional SkillsSuccessfully executed a data migration or modernization to Data Bricks, preferably IBM Data Stage to Data Bricks on AWS.Experience in handling large migrations to Data Bricks.Good analytical skills to compare the legacy and modern data platform end to end right from source to target.Good understanding of DataBricks implementation of Medallion layer architecture.Independently lead and managed large Data Bricks migrations.CI/CD Integration: Implement version control (e.g., Git) and automated deployment processes for Databricks assets.Technical and Architectural SkillsCore Data Engineering LanguagesExperience in advanced SQL for building modular analytics workflows, utilizing advanced Common Table Expressions (CTEs), and writing high-performance queries inside Data Bricks SQL Analytics.Experience in Python or Scala to build, optimize, and debug complex data transformation scripts, custom functions, and machine learning pipelines.Big Data & Architecture CoreExperience in Apache Spark Ecosystem for understanding cluster execution flow, memory allocation, driver/worker nodes, and handling data frames.Experience in Delta Lake Architecture to understand ACID transactions on object storage, data skipping, partition strategies, and automated data compaction.Databricks Platform ExpertiseExperience in Delta Live Tables (DLT) & Workflows for constructing and orchestrating production-ready, declarative streaming, and batch ETL pipelines.Experience in Unity Catalog for setting up data governance, column/row-level access control, and tracking end-to-end data lineage across workspaces.Experience in Auto Loader for implementing modern, incremental data ingestion patterns from cloud blob storage into the lakehouse.Code Translation & RefactoringPipeline ConversionTranslate visual DataStage Parallel Jobs and Sequences into Python/PySpark scripts or Data bricks Notebooks.Legacy RefactoringModernize legacy logic rather than applying "lift and shift" anti-patterns; adapt workflows to think in distributed DataFrames rather than DataStage stages.Logic MappingMap DataStage components—such as Aggregators, Joiners, Transformers, and Sort stages—to equivalent Spark operations.Testing & ReconciliationValidation & ReconciliationBuild automated reconciliation frameworks to compare row counts, checksums, and aggregate sums between legacy DataStage outputs and new Databricks output.Data CleansingIdentify and resolve data type discrepancies, null-handling differences, and encoding issues during the extraction and loading phases.Platform Orchestration & GovernanceOrchestrationReplace DataStage sequence jobs with Databricks workflows (or external orchestrators like Azure Data Factory/Airflow) to schedule and manage dependencies.Data GovernanceEnforce data lineage, security, and cataloging using Unity Catalog to ensure compliance in the new Lakehouse environment.Cloud Infrastructure & CI/CDCloud Providers (AWS): Understanding underlying cloud object storage, identity access management (IAM), and network security configurations.DevOps & Bundles: Familiarity with Databricks Asset Bundles (DABs) and CI/CD tools to automate the deployment of workspaces and pipeline assets.Legacy Assessment & Migration MechanicsCode Conversion & TranslationThe ability to parse legacy code structures and refactor them into Databricks-native code.AI-Assisted MigrationSkills in using AI coding assistants and open framework agent tools to analyze application interdependencies, automate schema mapping, and accelerate lift-and-shift workloads.Code Conversion & TranslationThe ability to parse legacy code structures from ETL pipelines, Informatica, data Stage preferred.Experience working in Agile teams and understanding of data governance frameworks.ResponsibilitiesPost-Migration SupportSupport post-migration environment from IBM DataStage to Databricks.Incident & Lifecycle ManagementCI/CD DeploymentSupport code deployments across Development, Test, and Production environments using Databricks Repos and REST APIs.Monitoring & AlertingSet up monitoring via Databricks System Tables and observability tools to catch job failures, data anomalies, or latency spikes early.Pipeline Maintenance & OrchestrationWorkflow ManagementTransition from DataStage job sequences to native data bricks workflows for scheduling, dependency tracking, and alerts.ETL RefactoringTroubleshoot and fix issues in generated PySpark or Spark SQL code that replaced legacy DataStage Transformer or Lookup stages.Streaming & Batch IntegrationSupport ongoing data ingestion using data bricks autoloader to process files continuously from cloud storage.Performance Tuning & Cost OptimizationCompute ManagementMonitor and configure serverless or classic clusters to prevent over-provisioning.Query OptimizationAnalyze Spark execution plans. Replace inefficient row-by-row processing logic (a common DataStage carryover) with vectorized operations and native Spark functions.Storage OptimizationMaintain Delta Lake tables by enforcing layout optimization (ZORDER).Data Governance & SecurityAccess ControlImplement granular permissions, column-masking, and row-level filters using Data bricks unity catalog to replace DataStage's legacy security policies.Data QualityUtilize Delta Live Tables (DLT) to build pipelines with built-in, declarative data quality expectations and monitoring.Additional SkillsExcellent communication skills.Ability to collaborate with legacy and modernize application teams and stakeholders.Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence, and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.