Description

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 deploymen 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 loading 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 policies · 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

Key Words to search in Resume

· Migrated IBM Data Stage to Data Bricks on AWS · IBM Datastage to Data Bricks Modernization and Migration · Post Production Support after Databricks Migration · CI/CD

Key Skills

Education

Bachelor's degree