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Head of Data and Knowledge Engineering

Do you want to develop cutting edge technology to support cutting edge science?
Do you have experience working with large research data sets of different omics types with an engineering, architecture and design approach? Are you at the same time looking for an opportunity to break new ground at the forefront of what is possible? If so, this is the job you have been waiting for. At DTU Biosustain in Lyngby, Greater Copenhagen you will unfold your skills as enabler for approx. 300 world-class scientists in Copenhagen and San Diego. Your personal efforts will enable and accelerate our work towards development of new environment friendly products, better and less energy consuming manufacturing processes, new medical treatments, climate friendly farming, etc.

Expand and operate infrastructure to support high-throughput and complex data
As Head of Data and Knowledge Engineering your focus is to expand and operate a next generation informatics infrastructure that enables efficient management of big data sets, big data analytics, computational modeling and knowledge management. More specifically, you will expand and manage a cutting-edge data infrastructure consisting of data lake, data warehouse and institutional knowledge graph linking internal data with universal knowledge. A lot of your work will be related to our cutting edge, integrated informatics platform Lifelike.

We have already come a long way but we need you to drive our next leap forward by applying your engineering perspective while working hands-on with architecture, design and implementation. Your primary tasks will be to:

  • Expand the Center’s data management capabilities to support high-throughput data generation, enabling efficient big data collection, processing and analysis, as well as exchange of data between institutes
  • Coordinate technical work activities between a small team involved in data architecture, data engineering, and computational method development
  • Ensure adequate data capacity planning and monitoring, database administration and backup
  • Manage a hybrid infrastructure consisting of multi-cloud and on-premise
  • Manage a database environment consisting of multiple database types
  • Implement data processing and analysis pipelines integrating multiple databases
  • Deploy and optimize big data analytics methods that are being developed by data scientists
  • Participate in the research and implementation of knowledge engineering approaches utilizing knowledge graphs, enabling the combination of graph analysis with AI to support explanatory AI systems.

  • To succeed, you must collaborate and communicate effectively with PhD level scientists. This calls for your ability to and motivation for understanding the highly complex and scientific data sets that your infrastructure shall handle. As you will be working with colleagues and partners in different locations, you must be able to handle some calls and remote meetings outside normal office hours.

    Solid experience working with large scale data platform components
    As you will be part of a support unit that works as enabler for numerous scientists across DTU’s many teams and locations, you must like to create results via knowledge sharing and constructive collaboration – with due respect to professional and cultural differences. This calls for an open-minded approach as well as your ability to tailor your communication to your audience. You are known for your analytical way of working and your positive belief in that there always is solution to a problem at hand. Related to this, you can establish KPIs, project plans and deliver as promised within agreed deadlines. Additionally, your CV comprises:

  • A university degree in Engineering or similar
  • Solid experience with designing, building, maintaining, and up scaling large scale data management platforms
  • Technical leadership experience
  • Experience in data modeling to meet large scale requirements
  • Background or experience in Biotech, Computational Biology or Bioinformatics
  • Experience managing large research data sets of different omics types (genomics, transcriptomics, proteomics, metabolomics, etc.)
  • Experience with machine learning, knowledge graphs/graph databases, or computational modeling is a big plus
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