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.

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.

• 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.

• 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 experience.

• 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.