data mapper-data engineer

Dear Candidates,

Current there is a on going vacancy for DATA MAPPER role-for a leading MNC on a direct payroll for bangalore location.

Interested candidates can share their resume with below mentioned mail id.

Mail ID: [email protected]

JD:

Position: Data Engineer-Data Mapper

Company Name: MNC(Service based)

Job Type: Permanent / Full time

Exp: 5+ yrs

Job Location: Bangalore

Job Mode: Hybrid-3 days WFO

Notice period-Serving notice period/ Immediate joiner

Interview Mode- Virtual(2 Rounds)-Technical

Salary: Up to 20 LPA(Negotiable)

Job Description

Core Skills

- Data extraction, ETL ELT, JSON, XML schema design, data mapping, and transformation,

- Data engineering: normalisation, schema JSON design, transformation logic.

- Data quality validation, taxonomyrules design, and enterprise data integration

Preferred Good to have Skills

• Deep semantic understanding of legacy Amadeus PMSC, OXI, or iHotelier configuration rules and data models, and how they map conceptually to Oracle OPERA Cloud database schemas.

• Hospitality domain exposure.

• AI Tool Usage and Collaboration: Hands-on AI Tooling.

Key responsibilities:

• Design, build, and maintain the discovery and mapping engine that automatically extracts legacy configurations, normalizes them, and classifies every data value so the factory pipelines can run automatically or flag items for expert human review.

• Design and develop data extraction, transformation, and normalization processes to support enterprise integrations and AI solutions.

• Legacy-to-Target Data Translation: Analyze legacy Amadeus data structures e.g., room types, rate codes, and guest profile hierarchies and design normalized mapping logic to import them cleanly into target OPERA Cloud configurations.

• Create and manage data schemas, JSON structures, mapping specifications, and transformation logic to ensure consistent data exchange across systems.

• Develop and maintain business rules, taxonomies, and data models to improve data interoperability and usability.

• Collaborate with integration, AI, and application teams to resolve mapping issues and optimize data flows.

• Support testing, troubleshooting, and documentation of data pipelines while adhering to data governance, security, and industry best practices.

• Collaborative Enablement: Partner with Dev, QA, and Ops teams to improve deployment efficiency, platform reliability, and overall engineering productivity.

• Continuous Feedback: Feed recurring payload and system failure signatures back to integration and AI engineers to optimize the core factory