Key Facts

Remote From:

Anywhere

Full time

Senior (5-10 years)

English

Hard Skills

Test Data Data Mapping Data Validation Azure Data Lake File Format JSON Java XML Data Transformation

Roles & Responsibilities

Strong data testing experience
Technical understanding of Azure Data Lake, MuleSoft, Databus
Experience with different file formats like JSON, XML
Building custom queries for data validation

Requirements:

Understanding of requirements and data mapping validation
Handling of historical data validation and incremental data validations
Performance testing for batch jobs and development of workload models
Creating baseline vs actual comparison reports

Georgia IT, Inc.

About Georgia IT, Inc.

Georgia IT, Inc. provides IT Consulting for a wide range of IT services and custom build turn-key enterprise solutions. GIT specializes in improving business scalability and efficiency through BSM and SBA Solutions. GIT transforms business with service management and service automation solutions. We are BMC & HP partners. GIT Services include custom built enterprise software and customer-centric web portals, network design and implementation, remote and site-to-site VPN, network and server security assessment and setup, server and desktop virtualization, and many others. GIT also provides IT Consulting in many areas such as Disaster Recovery Planning, Enterprise Data Backup Strategy, Long Term Strategic IT Planning and Augmentation Professional Services Solutions. Professional Services Specialties: Business Process Integration, Software Integration & Development, Web portal, Networking, Remote and site-to-site VPN, Virtualization Solutions, Product Development Please contact us Hrus@georgiait.com / uthay@georgiait.com/470-798-5000 x 1010 / (732) 890-2535 direct

Founded: 2018

Company size: 51 - 200

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Job description

Job Tittle: Data Testing Lead (Remote)
Location: Chicago, IL
Duration: 6-12 Months

• Strong data testing experience – Understanding of requirements, data mapping validation, data transformation rules, and downstream validation
• Technical understanding of the platforms and files – Azure Data Lake, MuleSoft, Databus, handling of different file formats, JSON, XML etc
• Building custom queries for data validation between different types of sources and target systems
• Understanding of historical data validation, full data, and incremental data validations
• Performance testing for batch jobs – Understanding of current state requirements, development of workload models, associated metrics, baseline vs actual comparison reports etc

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