Job Summary:
Tata Consultancy Services is seeking a Data Bricks Migration and Support Engineer to lead and manage large data migrations to Data Bricks, specifically from IBM DataStage. The role involves executing data migration projects, ensuring data governance, and optimizing performance while supporting the post-migration environment.
Responsibilities:
• Support post-migration environment from IBM DataStage to Databricks
• CI/CD Deployment: Support code deployments across Development, Test, and Production environments using Databricks Repos and REST APIs
• Monitoring & Alerting: Set up monitoring via Databricks System Tables and observability tools to catch job failures, data anomalies, or latency spikes early
• Workflow Management: Transition from DataStage job sequences to native data bricks workflows for scheduling, dependency tracking, and alerts
• ETL Refactoring: Troubleshoot and fix issues in generated PySpark or Spark SQL code that replaced legacy DataStage Transformer or Lookup stages
• Streaming & Batch Integration: Support ongoing data ingestion using data bricks autoloader to process files continuously from cloud storage
• Compute Management: Monitor and configure serverless or classic clusters to prevent over-provisioning
• Query Optimization: Analyze Spark execution plans. Replace inefficient row-by-row processing logic (a common DataStage carryover) with vectorized operations and native Spark functions
• Storage Optimization: Maintain Delta Lake tables by enforcing layout optimization (ZORDER)
• Access Control: Implement granular permissions, column-masking, and row-level filters using Data bricks unity catalog to replace DataStage's legacy security policies
• Data Quality: Utilize Delta Live Tables (DLT) to build pipelines with built-in, declarative data quality expectations and monitoring
Qualifications:
Required:
• Successfully 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
• Experience 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
• Experience 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
• Experience 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
• Pipeline Conversion: Translate visual DataStage Parallel Jobs and Sequences into Python/PySpark scripts or Data bricks Notebooks
• Legacy Refactoring: Modernize legacy logic rather than applying 'lift and shift' anti-patterns; adapt workflows to think in distributed DataFrames rather than DataStage stages
• Logic Mapping: Map DataStage components—such as Aggregators, Joiners, Transformers, and Sort stages—to equivalent Spark operations
• Validation & Reconciliation: Build automated reconciliation frameworks to compare row counts, checksums, and aggregate sums between legacy DataStage outputs and new Databricks output
• Data Cleansing: Identify and resolve data type discrepancies, null-handling differences, and encoding issues during the extraction and loading phases
• Orchestration: Replace DataStage sequence jobs with Databricks workflows (or external orchestrators like Azure Data Factory/Airflow) to schedule and manage dependencies
• Data Governance: Enforce data lineage, security, and cataloging using Unity Catalog to ensure compliance in the new Lakehouse environment
• Cloud Providers (AWS): Understanding underlying cloud object storage, identity access management (IAM), and network security configurations
• Familiarity with Databricks Asset Bundles (DABs) and CI/CD tools to automate the deployment of workspaces and pipeline assets
• The ability to parse legacy code structures and refactor them into Databricks-native code
• Skills in using AI coding assistants and open framework agent tools to analyze application interdependencies, automate schema mapping, and accelerate lift-and-shift workloads
• Experience working in Agile teams and understanding of data governance frameworks
• Support post-migration environment from IBM DataStage to Databricks
• CI/CD Deployment: Support code deployments across Development, Test, and Production environments using Databricks Repos and REST APIs
• Set up monitoring via Databricks System Tables and observability tools to catch job failures, data anomalies, or latency spikes early
• Transition from DataStage job sequences to native data bricks workflows for scheduling, dependency tracking, and alerts
• Troubleshoot and fix issues in generated PySpark or Spark SQL code that replaced legacy DataStage Transformer or Lookup stages
• Support ongoing data ingestion using data bricks autoloader to process files continuously from cloud storage
• Monitor and configure serverless or classic clusters to prevent over-provisioning
• Analyze Spark execution plans. Replace inefficient row-by-row processing logic (a common DataStage carryover) with vectorized operations and native Spark functions
• Maintain Delta Lake tables by enforcing layout optimization (ZORDER)
• Implement granular permissions, column-masking, and row-level filters using Data bricks unity catalog to replace DataStage's legacy security policies
• Utilize Delta Live Tables (DLT) to build pipelines with built-in, declarative data quality expectations and monitoring
• Excellent communication Skills
• Ability to collaborate with Legacy and Modernize application teams and stakeholders
• BACHELOR OF COMPUTER SCIENCE
Company:
Tata Consultancy Services is a business solutions company that specializes on information technology services and consulting. It is a sub-organization of Tata Group. Founded in 1968, the company is headquartered in Mumbai, IND, with a team of 10001+ employees. The company is currently Late Stage.