Live opening · Posted 7 days ago

Technical Architect

Mphasis · Pune Division, Maharashtra, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 7 days ago
CompanyMphasis
LocationPune Division, Maharashtra, India (On-site)
Work modeNo
SourceLinkedin
Listed7 days ago

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About the role

Description supplied by the original job listing.

Role description
Senior Databricks Engineer – SAS Modernisation & Migration
Role Summary
Looking for a strong Databricks Engineer who understands how SAS platforms work and can modernise SAS workloads into Databricks. The role covers discovery, migration, validation, optimisation and production deployment of SAS workloads onto Databricks. The individual should be capable of understanding SAS business logic and rebuilding it efficiently using Databricks, PySpark and modern data engineering practices.
Core Skills (Must Have)
Databricks (Primary Skill)
Azure Databricks / Databricks Lakehouse
PySpark
Spark SQL
Python
Delta Lake
Unity Catalog
Databricks Workflows
Medallion Architecture (Bronze/Silver/Gold)
Performance tuning and optimisation
CI/CD and Git-based development
Production support and troubleshooting
SAS (Strong Working Knowledge)
Base SAS
SAS DATA Step
PROC SQL
SAS Macros
SAS Enterprise Guide
SAS Grid / SAS Viya
Batch scheduling and job dependencies
SAS datasets, libraries and file processing
Understanding of SAS business logic and data lineage
Key Responsibilities
SAS Discovery & Assessment
Analyse SAS estate and identify dependencies, business logic and migration complexity.
Inventory SAS jobs, datasets, macros and interfaces.
Classify workloads for retire, refactor, re-engineer or migrate.
SAS to Databricks Migration
Convert SAS code to PySpark/Spark SQL.
Re-engineer legacy SAS processing into scalable Databricks solutions.
Develop Delta Lake pipelines and Databricks workflows.
Support automated conversion and manual remediation where required.
Validation & Reconciliation
Compare SAS and Databricks outputs.
Build automated reconciliation frameworks.
Perform record count, aggregation and business logic validation.
Support UAT and business sign-off.
Architecture & Engineering
Design Databricks Lakehouse solutions.
Implement governance using Unity Catalog.
Optimise performance, scalability and cost.
Support Dev, Test and Production deployments.
Experience Required
8+ years in Data Engineering / Analytics.
4+ years hands-on Databricks experience.
3+ years working with SAS environments.
Previous experience in SAS to Databricks, SAS to PySpark or similar data-platform modernisation programs.
Experience in large enterprise environments involving migration, reconciliation and production cutovers.

Work arrangement
No

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