Data Migration Specialist

Virtusa Corporation; Hyderabad; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Virtusa Corporation seeks a senior Data Engineer to design, map, and validate ETL processes across multiple workstreams. The role focuses on transforming source extracts into clean, reconciled loads for target systems, including ERP and CRM platforms.

The ideal candidate has 6+ years in data engineering or ETL, strong SQL and Python skills, and experience with data profiling and cloud-based extracts (AWS S3). Remote options may apply depending on client needs.

Qualifications

• 6+ years in data engineering or ETL, including at least two production data migrations.
• Strong SQL and Python (or a comparable scripting language) for transformation and validation.
• Experience loading data into an ERP such as SAP (LSMW, BAPIs, Migration Cockpit or IDocs) or a CRM such as Salesforce (Data Loader, Bulk API).
• Working knowledge of data profiling, de-duplication and reconciliation techniques.
• Comfortable working with file-based extracts in cloud storage such as AWS S3.

Responsibilities

• Map source attributes to the target data dictionary for assigned entities, such as customers, licences, products, orders or contracts.
• Profile and cleanse source data: duplicates, missing fields, invalid codes and inconsistent formats.
• Build transformation and load scripts into target systems such as ERP, CRM, legacy platforms and e-commerce.
• Match source customers against existing target records and apply cross-system ID mapping.
• Run QA and mock loads; produce record counts, field-level reconciliation and exception reports.
• Log and fix defects, feeding remapping needs back to source-system owners.
• Automate repeatable steps, such as bulk record setup in place of manual entry.
• Document mappings, transformation rules and load procedures for cutover and support handover.

Skills

SQL

Python

Data profiling

Data reconciliation

ETL

Tools

SAP ERP

Salesforce

AWS S3

Job description

Role summary: Builds and runs the mapping, transformation, load and validation for one or more workstreams. Turns source extracts into clean, reconciled loads in target systems.

Key responsibilities

• Map source attributes to the target data dictionary for assigned entities, such as customers, licences, products, orders or contracts.
• Profile and cleanse source data: duplicates, missing fields, invalid codes and inconsistent formats.
• Build transformation and load scripts into target systems such as ERP, CRM, legacy platforms and e-commerce.
• Match source customers against existing target records and apply cross-system ID mapping.
• Run QA and mock loads; produce record counts, field-level reconciliation and exception reports.
• Log and fix defects, feeding remapping needs back to source-system owners.
• Automate repeatable steps, such as bulk record setup in place of manual entry.
• Document mappings, transformation rules and load procedures for cutover and support handover.

Required qualifications

• 6+ years in data engineering or ETL, including at least two production data migrations.
• Strong SQL and Python (or a comparable scripting language) for transformation and validation.
• Experience loading data into an ERP such as SAP (LSMW, BAPIs, Migration Cockpit or IDocs) or a CRM such as Salesforce (Data Loader, Bulk API).
• Working knowledge of data profiling, de-duplication and reconciliation techniques.
• Comfortable working with file-based extracts in cloud storage such as AWS S3.

Preferred qualifications

• Legacy platform experience, such as AS400/IBM i (CL, RPG or DB2 for i).
• Experience with Kafka or other integration platforms.
• Exposure to entitlement data: institutional licences, IP ranges, SSO or consortia affiliations.
• Familiarity with a cloud data warehouse such as BigQuery for reconciliation and reporting.
• Domain knowledge in subscription, licensing or content-based businesses.

Success measures

• Mappings complete and signed off for assigned entities.
• Each load meets agreed reconciliation thresholds in QA before production.
• Defects resolved within agreed turnaround during mock cycles.