Data Quality Assurance Specialist (Laboratory Data)
About this role
This is a remote position.
We are looking for an experiencedData Consultantto join a global pharmaceutical client in Prague.
The role focuses on the quality, validation, and onboarding ofscientific and laboratory data, including data generated by laboratory instruments and transformed into standardizedAllotrope Simple Model (ASM)datasets.
You will work closely withScientists, Scientific SMEs, Semantic Engineers, Data Engineers, Quality Engineering teams, Product Owners, and external vendorsto define data requirements, validate transformations, ensure data integrity, and support the successful delivery of high-quality scientific data solutions.
The position combinesdata quality, data validation, data integration, scientific data mapping, stakeholder management, and delivery coordination.
Key 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 on mapping data to theAllotrope Ontologyand ASM schemas.
Validate converter outputs and ensure scientific meaning and data integrity are preserved.
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Perform and coordinate:
Source-to-target validation
Data transformation verification
Data quality checks
Traceability testing
Semantic mapping validation
Converter output validation
Review test results, validation evidence, defects, and data quality metrics.
Support defect triage, root-cause analysis, risk assessment, and remediation.
Ensure appropriate traceability between requirements, mappings, test scenarios, and validation evidence.
Support release-readiness assessments and approval processes.
Create and review SDLC and validation documentation.
Manage risks, issues, dependencies, and delivery blockers.
Coordinate activities across Scientific, Data Engineering, Quality, Product, Architecture, and vendor teams.
Support continuous improvement of data quality processes, validation methodologies, and reusable testing assets.
Ensure adherence to data governance, SDLC, quality, and compliance requirements.
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 on mapping data to theAllotrope Ontologyand ASM schemas.
Validate converter outputs and ensure scientific meaning and data integrity are preserved.
Perform and coordinate:
Source-to-target validation
Data transformation verification
Data quality checks
Traceability testing
Semantic mapping validation
Converter output validation
Review test results, validation evidence, defects, and data quality metrics.
Support defect triage, root-cause analysis, risk assessment, and remediation.
Ensure appropriate traceability between requirements, mappings, test scenarios, and validation evidence.
Support release-readiness assessments and approval processes.
Create and review SDLC and validation documentation.
Manage risks, issues, dependencies, and delivery blockers.
Coordinate activities across Scientific, Data Engineering, Quality, Product, Architecture, and vendor teams.
Support continuous improvement of data quality processes, validation methodologies, and reusable testing assets.
Ensure adherence to data governance, SDLC, quality, and compliance requirements.
Requirements
Bachelor's or master's degree inComputer Science, Engineering, Data Science, Information Systems, Biotechnology, or a related field, or equivalent professional experience.
6+ years of experiencein Data Engineering, Data Integration, Data Management, Data Quality, or related data-focused roles.
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Strong understanding of:
Data quality
Data validation
Data modeling
Data mapping
Data transformation
Source-to-target mapping
Data traceability
Experience defining requirements, acceptance criteria, validation approaches, and test scenarios.
Experience validating complex data transformations and integrations.
Understanding of thesoftware development lifecycle (SDLC)and data lifecycle management.
Knowledge of data governance principles.
Experience working with non-technical stakeholders to understand business or scientific requirements.
Ability to translate business or scientific requirements into clear data and validation specifications.
Experience documenting and managing risks, issues, dependencies, and decisions.
Strong analytical and problem-solving skills.
Strong communication and stakeholder management skills.
Experience working with cross-functional teams including Engineering, Quality, Product, Business, SMEs, and external vendors.
Nice to Have
Experience withinpharmaceutical, biotechnology, life sciences, or drug discoveryenvironments.
Experience working with scientific or laboratory data.
Understanding of laboratory workflows and laboratory instrument data.
Experience with data onboarding, integration, migration, transformation, or validation projects.
Knowledge ofAllotrope Foundation standards.
Experience withAllotrope Simple Model (ASM).
Knowledge of ontologies, semantic models, or metadata management.
Experience withJira, Confluence, VERA, Xray, Mural, or similar tools.
Experience with automated data validation or testing frameworks.
Experience withinpharmaceutical, biotechnology, life sciences, or drug discoveryenvironments.
Experience working with scientific or laboratory data.
Understanding of laboratory workflows and laboratory instrument data.
Experience with data onboarding, integration, migration, transformation, or validation projects.
Knowledge ofAllotrope Foundation standards.
Experience withAllotrope Simple Model (ASM).
Knowledge of ontologies, semantic models, or metadata management.
Experience withJira, Confluence, VERA, Xray, Mural, or similar tools.
Experience with automated data validation or testing frameworks.
Ideal Candidate
The ideal candidate has a strong background indata quality and data integrationand is comfortable working with complex datasets and stakeholders from both technical and scientific backgrounds.
Experience with pharmaceutical or laboratory data is highly valuable. Direct Allotrope / ASM experience is preferred but not mandatory if you bring strong expertise indata mapping, transformation, validation, traceability, and data quality assurance.
Benefits
- B2B Contract
- Remote
- 40 h/week
- Start: in max 1 month
- Top 5 Global pharma company
- Scientific project
Originally posted on Himalayas
