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Summary

QIAGEN Digital Insights is a leading provider of bioinformatics software and knowledge bases used by life scientists to gain insight from the molecular information in their biological samples. We have developed industry-leading software tools for analysis and reporting of biological data. We are passionate about our users, our products, and our vision, and are seeking smart, motivated engineers and scientists who are eager to join our team in creating software that helps improve people's lives.

Our mission is to make improvements in life possible. We accomplish this by designing tools used in labs all over the world to facilitate scientific discovery and progress, focused on genomics.

As the Test Automation Engineering Manager, you will lead a team of 3-5 Test Automation Engineers and QA/QC specialists responsible for validating the scientific quality, correctness, and scalability of our biological content, analytics, and data pipelines. You will also remain a hands-on technical contributor, helping to develop scalable methods to assess the correctness and completeness of our biological information and the accuracy and reliability of our analytics and interpretations. You will be part of a collaborative, cross-functional team of content scientists, knowledge engineers, and software engineers building powerful tools to gain insight into biomolecular data. We have built a world-class Knowledge Base rich with molecular and functional relationships derived from scientific literature and biological databases. In combination with our Knowledge Base, we use semantic technologies and other computational techniques to provide biological context to our analytics. This is an excellent opportunity to be directly involved in developing our technology, delivering a high-quality product, and making an impact on human healthcare.

Responsibilities

Team Leadership & Management:

• Lead, mentor, and develop a team of 3-5 Test Automation and QA/QC engineers.
• Define team priorities, testing strategies, and quality goals.
• Partner with cross-functional stakeholders to coordinate testing coverage and represent content QA in release planning and tracking.

Technical Leadership & Execution

• Review and refine data and software requirements, use cases, and workflows for product features to ensure that they are scientifically valid, technically robust, and testable.
• Design and implement automated quality controls and improve reliability of processes, pipelines, tools, and models that support development, build, and maintenance of our biological content.
• Own the test automation lifecycle for specific data, product features, or tools, taking individual responsibility to specify and execute the testing approach in design, implementation, and reporting.
• Select or develop test automation tools and frameworks. Contribute to maintenance of existing continuous integration pipelines and infrastructure.
• Document testing and validation efforts according to appropriate quality management frameworks and regulations (ISO 9001, ISO 13485, IVDR)

Your Profile

• Undergraduate degree in bioinformatics, bioengineering, genetics, molecular biology, or a related field. Graduate degree preferred.
• 5+ years of industry experience in bioinformatics, including experience developing test automation for production-grade bioinformatics tools and pipelines.
• 2+ years of experience managing engineers or bioinformatics scientists.
• Strong software development fundamentals, with experience in multiple general-purpose programming languages, including Python, and comfort operating in a Linux environment. Experience with Perl and Java is a plus. Mastery of software engineering best practices such as version control (git), code review, unit and integration testing, documentation, and collaborative development.
• Domain knowledge of human genetics and genomics, and familiarity with tools and databases used in genomic biomedical research (e.g., NCBI, Ensembl, RefSeq, ClinVar, COSMIC, OMIM).
• Practical experience with biomedical ontologies (e.g., Gene Ontology, Human Phenotype Ontology) and related semantic technologies such as RDF, OWL, and SPARQL.
• Experience with multiple data modeling paradigms, including relational databases (SQL) and non-relational approaches such as graph databases (e.g., Neo4j).
• Experience with information extraction and knowledge modeling of structured and unstructured biological data sources is preferred.
• Experience with API testing and browser-based test automation tools such as Selenium and WebDriver is a plus.