Exp. Required: 8+ Year’s
Must Have Technical/Functional Skills
• Successfully executed a data migration or modernization to Data Bricks, preferably IBM Data Stage to Data Bricks on AWS
• Should have Experience in handling Large Migrations to Data Bricks.
• Should have 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 skills required are below.
Core Data Engineering Languages
• 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.
Big Data & Architecture Core
• 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.
Databricks Platform Expertise
• 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.
Code Translation & Refactoring
• 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
Testing & Reconciliation
• 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 loadi
ng phases
Platform Orchestration & Governance
• 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.
GOOD TO Cloud Infrastructure & CI/CD
• Cloud 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 Mechanics
• Code Conversion & Translation: The ability to parse legacy code structures and refactor them into Databricks-native code.
AI-Assisted Migration: Skills 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 & Translation: The ability to parse legacy code structures from ETL pipelines, Informatica, data Stage preferred
Experience working in Agile teams and understanding of data governance frameworks.
Responsibilities
Support post-migration environment from IBM DataStage to Databricks
Incident & Lifecycle Management
• 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
Pipeline Maintenance & Orchestration
• 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
Performance Tuning & Cost Optimization
• 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 (\(ZORDER\)
Data Governance & Security
• Access Control: Implement granular permissions, column-masking, and row-level filters using Data bricks unity catalog to replace DataStage's legacy security p
olicies
• Data Quality: Utilize Delta Live Tables (DLT) to build pipelines with built-in, declarative data quality expectations and monitoring
Additional Skills
• Excellent communication Skills
• Ability to collaborate with Legacy and Modernize application teams and stake holders
