Description review
Data Migration Engineer (Fall 2026) (Remote, KA, IN)
NTT DATA · India · back to the listing
HR standards
53/100
needs work
Title ↔ description
73/100
solid
Reads as
Data Engineer
100% confident
How others title the same work
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What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Key Responsibilities
• Assess complex Informatica workflows including sessions, mappings, and parameterizations and translate them into equivalent Python EL scripts and dbt transformation models.
• Develop and maintain Python EL pipelines to land high-volume data, including billion-row tables, into the target warehouse using SQLAlchemy, cx_Oracle, pyodbc, and bulk export tools such as Oracle Data Pump and SQL Server BCP.
• Design and develop dbt transformation models based on Informatica mapping logic, incorporating dbt best practices including model layering, macros, incremental strategies, and snapshot patterns.
• Develop dbt-native tests as well as custom Python unit tests to validate transformation correctness and data quality.
• Develop Airflow DAGs to orchestrate Python EL and dbt scripts end-to-end, producing output that is functionally equivalent to the source Informatica workflows.
• Contribute to GitLab CI/CD pipeline for dbt and Airflow code, including lint gates, automated testing, and deployment to shared NAS.
• Perform peer code reviews and provide constructive technical feedback to fellow engineers.
• Troubleshoot performance issues and data discrepancies during SIT and UAT, including row-count reconciliation between source Oracle/SQL Server systems and the target warehouse.
• Contribute to technical documentation, runbooks, and handover materials.
Originally posted on Himalayas
• Assess complex Informatica workflows including sessions, mappings, and parameterizations and translate them into equivalent Python EL scripts and dbt transformation models.
• Develop and maintain Python EL pipelines to land high-volume data, including billion-row tables, into the target warehouse using SQLAlchemy, cx_Oracle, pyodbc, and bulk export tools such as Oracle Data Pump and SQL Server BCP.
• Design and develop dbt transformation models based on Informatica mapping logic, incorporating dbt best practices including model layering, macros, incremental strategies, and snapshot patterns.
• Develop dbt-native tests as well as custom Python unit tests to validate transformation correctness and data quality.
• Develop Airflow DAGs to orchestrate Python EL and dbt scripts end-to-end, producing output that is functionally equivalent to the source Informatica workflows.
• Contribute to GitLab CI/CD pipeline for dbt and Airflow code, including lint gates, automated testing, and deployment to shared NAS.
• Perform peer code reviews and provide constructive technical feedback to fellow engineers.
• Troubleshoot performance issues and data discrepancies during SIT and UAT, including row-count reconciliation between source Oracle/SQL Server systems and the target warehouse.
• Contribute to technical documentation, runbooks, and handover materials.
Originally posted on Himalayas