QA Engineer, Senior

Infor Inc.; Hyderabad; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Infor Inc. is seeking a Senior QA Engineer to validate ontology and canonical data artifacts within the Data Fabric. You will perform semantic testing, cross-ERP mapping validation, and develop regression suites. A structured ramp program will teach SPARQL/SHACL testing and data-quality techniques.

The role emphasizes data modeling, testing automation, and collaboration with engineers on acceptance criteria, with AWS basics and Java/Spring services context.

Qualifications

• 5+ years of QA/testing experience.
• Experience testing data quality, ETL, or data integration projects.
• Strong SQL skills and basic test-automation scripting ability.
• Familiarity with test automation and CI/CD pipelines.
• Exposure to graph databases, RDF, or rule-based validation.
• Experience with ERP data models.

Responsibilities

• Semantic web/ontology testing with SPARQL/SHACL: write and validate rules.
• Canonical data modeling and cross-ERP mapping validation.
• Design tests, manage defects, and build regression suites.
• Support CI/CD-integrated test automation and tooling.
• Work with SQL and Java/Spring services; understand AWS basics.
• Validate data pipelines in Infor Data Fabric platform.

Skills

QA testing

Data quality

SQL

Scripting basics

CI/CD

Semantic data

Learning agility

Tools

SHACL

SPARQL

Apache Jena

Fuseki

SQL tooling

Version control

Job description

QA Engineer, Senior

Department: Development

Employment Type: Full Time

Location: Hyderabad

Description

The Quality Senior Analyst — Ontology & Canonical Data Modeling owns validation and quality assurance for Infor's Data Fabric ontology and canonical data model artifacts, including SHACL/SPARQL-based test assertions and cross-ERP mapping validation. The role requires 5+ years of QA/data-quality testing experience; no prior ontology/SPARQL experience is required, as this is built through a structured internal ramp program.

A Typical Day in the Life Includes:

Semantic web / ontology testing (learned on the job): SPARQL-based test assertions, SHACL constraint validation, Apache Jena, Fuseki

Canonical data modeling: Cross-ERP mapping validation, master data concepts, data lineage

Testing & automation: Test design, defect management, regression suites, CI/CD-integrated test automation

Application context: SQL, basic scripting; working familiarity with Java/Spring-based services under test

Cloud platform: AWS (or equivalent) — basic working knowledge

Data Fabric platform: Infor Data Fabric integration pipelines and canonical schema examples

• Progress through the Basic → Advanced learning map with a quality/testing focus, passing checkpoints on schedule.
• Author and maintain SHACL/SPARQL-based validation rules and regression test suites.
• Validate canonical mappings for completeness and correctness across ERP source systems.
• Track, report, and drive resolution of data-quality defects and mapping gaps.
• Partner with engineers on acceptance criteria and quality standards for new deliverables.

Basic Qualifications:

• 5+ years of QA/testing experience.
• Experience testing data quality, ETL, or data integration/migration projects.
• Strong SQL skills and basic test-automation scripting ability.
• Strong analytical and defect-documentation skills.
• Demonstrated learning agility to pick up SPARQL/SHACL-based testing techniques.
• Experience testing master data management or cross-system integration projects.
• Familiarity with test automation frameworks and CI/CD pipelines.
• Any prior exposure to graph databases, RDF, or rule-based validation.
• Experience with ERP data models.

Preferred Qualifications:

• Master data management or cross-system integration testing.
• Test automation and CI/CD-integrated quality gates.
• Exposure to graph/semantic data or rule-based validation systems.
• ERP-domain testing.
• On-time progression through the learning-map checkpoints relevant to quality/testing.
• Defect detection rate and quality of defect reports for canonical/ontology artifacts.
• Coverage and reliability of SHACL/SPARQL-based regression test suites.
• Reduction in production data-quality escapes over time.
• Builds reliable, maintainable validation suites for evolving ontology and canonical artifacts.
• Becomes the team's go-to expert for semantic-data quality and regression risk.
• Partners effectively with engineers to shift quality left in the delivery process.
• Clearly documents and communicates data-quality risks to the Team Leader and stakeholders.