Data Analyst/Engineer (Laboratory instrument data)

We are looking for an experienced Data Quality Analyst to join a global pharmaceutical client in Prague.

The role focuses on the quality, validation, and onboarding of scientific and laboratory data, including data generated by laboratory instruments and transformed into standardized Allotrope Simple Model (ASM) datasets.

You will work closely with Scientists, Scientific SMEs, Semantic Engineers, Data Engineers, Quality Engineering teams, Product Owners, and external vendors to define data requirements, validate transformations, ensure data integrity, and support the successful delivery of high-quality scientific data solutions.

The position combines data 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 the Allotrope Ontology and 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 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

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) 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 within pharmaceutical, biotechnology, life sciences, or drug discovery environments.

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 of Allotrope Foundation standards.

Experience with Allotrope Simple Model (ASM).

Knowledge of ontologies, semantic models, or metadata management.

Experience with Jira, Confluence, VERA, Xray, Mural, or similar tools.

Experience with automated data validation or testing frameworks.

Ideal Candidate

The ideal candidate has a strong background in data quality and data integration and 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 in data mapping, transformation, validation, traceability, and data quality assurance.

Benefits

• Location: Hybrid/Remote
• Contract Type: Freelance / Contract
• Start date: October/November, 2026
• Time Allocation: 40 hours/week
• Global Pharmaceutical Company in Prague

This is a remote position.