Data Migration Engineer
NTT DATA — Bengaluru, Karnataka
₹3–7 LPA (HireSetu estimate) · Entry level · Freshers eligible · Full-time
HireSetu listing reference 83fe9c — confirm requirements below, then apply on the employer site.
Quick answer: A migration job where the migration is a rewrite: retiring Informatica mappings get rebuilt as Python extraction pipelines, dbt models and Airflow DAGs, with billion-row tables and row-count reconciliation as the proof it works.
From NTT DATA's job posting
Data Migration Engineer - Nov Expanded Team
Date: Nov 2, 2026
Location: Bangalore, KA, IN
Company: NTT DATA Services
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.
Text from NTT DATA's official job posting, formatted by HireSetu. The employer's own page is the final word on details.
The old mappings are the spec
Data migration in most Indian job ads means an SAP cutover or a database upgrade with a tool doing the lifting. Here the target stack is Python for extraction and loading, dbt for transformation, Airflow for orchestration, and the Informatica workflows being retired are what define correctness. Your first task on any pipeline is reading someone else's sessions, mappings and parameterizations and working out what they do.
If you have only built pipelines from a written spec, that reversal takes adjusting. Worth one question when you speak to them: whether the workflows come with documentation, or whether you reconstruct the logic by inspection.
Billion-row tables set the bar
The listing names billion-row tables and the bulk tools that go with them: Oracle Data Pump, SQL Server BCP, plus SQLAlchemy, cx_Oracle and pyodbc. At that volume the load strategy decides whether a run finishes at all, and dbt incremental models and snapshots are where the design work sits.
Tests are part of the build, not a phase at the end: dbt-native tests and custom Python unit tests, with lint gates in the GitLab CI pipeline.
Expect SIT and UAT to be judged on row counts between source Oracle or SQL Server and the target warehouse. Be fluent in joins, counts and diffs across two systems. Reconciliation is a skill of its own here, and it will take up more of your week than the code does.
Prepared from NTT DATA's job posting and checked against it before publishing. See our Editorial Policy.
How to apply
- The Apply button opens this job on NTT DATA's careers site (careers-inc.nttdata.com).
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