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Data Quality Specialist

2 months ago


Didcot, Oxfordshire, United Kingdom Scot Lewis Associates Full time £40,000
Job Description

**Data Steward - Scot Lewis Associates**

We are seeking a highly motivated and detail-oriented Data Steward to join our dynamic data interoperability team within a cutting-edge genetics research network. This role offers a unique opportunity to develop expertise in data interoperability and data modelling, focusing on integrating diverse datasets at scale.

Key Responsibilities:

  • Identify and escalate data issues to project stakeholders, ensuring data quality and integrity based on established validation metrics.
  • Collaborate with various stakeholders to survey metadata and data model requirements for a wide range of scientific data modalities.
  • Contribute to developing and implementing new standards for annotating scientific datasets.
  • Build and maintain a technical pipeline for mapping relational databases to the Resource Description Framework (RDF) and linking data to create AI-ready knowledge graphs.
  • Develop and enhance technical skills in Python, SQL, RDF, R, and database management, among other areas as required.
  • Produce high-quality documentation for collaborative open-source projects and maintain resources using version control systems like Git.
  • Communicate effectively with stakeholders and the wider scientific community through various platforms.
  • Ensure that all data-capture software and data-linking pipelines comply with FAIR principles.
  • Work systematically to harmonize metadata across various data types and consult with experts within the network.
  • Provide comprehensive support for data uploads, corrections, and troubleshooting, as needed for developmental projects.

Requirements:

  • A strong life sciences or computational degree, or equivalent relevant work experience.
  • Proven experience in building and managing technical pipelines for data curation and management.
  • SQL, database management, RDF, SPARQL, or similar.
  • Experience and knowledge working with complex datasets, including big data for transcriptomics, time-series data, and genomics.
  • Familiarity with linked data and controlled vocabularies.
  • Ability to develop and maintain professional working relationships and adapt to evolving project needs.