Data Quality Assurance Specialist (Laboratory Data)
New
F
futureproofPharmaceutical data
Source API remote eligibility restrictions: United StatesContractMiddle
Salary not disclosed
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Job Details
- Experience
- 6+ years of experience
- Required Skills
- Data modeling
Requirements
- Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, Information Systems, Biotechnology, or a related field, or equivalent professional experience.
- 6+ years of experience in Data Engineering, Data Integration, Data Management, Data Quality, or related data-focused roles.
- Strong understanding of data quality, data validation, data modeling, data mapping, data transformation, source-to-target mapping, and data traceability.
- Experience defining requirements, acceptance criteria, validation approaches, and test scenarios.
- Experience validating complex data transformations and integrations.
- Understanding of the software development lifecycle (SDLC), data lifecycle management, and data governance principles.
- Experience working with non-technical stakeholders to understand business or scientific requirements and translate them into data and validation specifications.
- Experience documenting and managing risks, issues, dependencies, and decisions.
- Experience working with cross-functional teams, including Engineering, Quality, Product, Business, SMEs, and external vendors.
- Pharmaceutical, biotechnology, life sciences, scientific or laboratory data experience is desirable.
- Knowledge of Allotrope Foundation standards, Allotrope Simple Model (ASM), ontologies, semantic models, or metadata management is desirable.
- Experience with Jira, Confluence, VERA, Xray, Mural, or similar tools, and automated data validation or testing frameworks, is desirable.
Responsibilities
- Define data quality requirements, acceptance criteria, validation approaches, and test scenarios.
- Support end-to-end onboarding and validation of scientific data and ASM converters.
- Lead requirements and discovery sessions with Scientists, SMEs, and business stakeholders.
- Analyze laboratory instrument data and define key scientific data fields.
- Create and review source-to-target mappings and data transformation requirements.
- Collaborate with Semantic Engineers to map data to the Allotrope Ontology and ASM schemas.
- Validate converter outputs and ensure scientific meaning and data integrity are preserved.
- Review test results, validation evidence, defects, and data quality metrics; support triage, root-cause analysis, risk assessment, and remediation.
- Maintain traceability across requirements, mappings, test scenarios, and validation evidence, and support release-readiness assessments.
- Coordinate cross-functional delivery, manage risks and dependencies, and support continuous improvement of data quality processes.
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