The Data Science Developer designs, develops, implements and continuously refines data warehouse architecture, data integration methods, scientific computing platforms and frameworks, analysis pipelines, user-facing applications, and end-to-end data flows.
- Work with other developers and data scientists to code proof-of-concept projects on large scale data sets.
- Develop data processing and system integration applications.
- Construct web based user interfaces and visualizations.
- Document design decisions, code, and workflows.
- Migrate successful proof-of-concept projects into production environments.
- Quickly ingest new technologies to consider applicability to current or future needs.
- Utilize statistics and predictive analytics to create innovative solutions to business problems.
- Analyze business requirements as they relate to the ETL process; research, evaluate, and recommend alternative solutions.
- Design, develop, and test ETL applications consistent with application architecture guidelines.
- Participate in an agile-based approach implementing data services and solutions to deliver value rapidly
- Implement a process to collect and maintain inventory of digital assets across the enterprise
- Implement ETL processes, including data acquisition, data integration, data quality testing, data indexing and query answering.
- Develop innovative data management, with real-time and predictive analytic capabilities
- Advise researchers in the preparation and execution of their research data management plans
- Collaborate with IT colleagues from Maastricht University and Maastricht University Medical Center
- 3+ years’ experience of IT platform development
- Strong verbal and written communications skills
- High quality standards
- Bring a versatile toolbox with you. Know how to select and apply the best and most efficient tool for the job ahead (UML & ER diagrams, Epics, User Stories, UI Mockups, automated Unit and User Acceptance testing)
- Experience with Continuous Delivery (Bug Tracker, SCM, automated Test and Build tools)
- Understanding of database and analytical technologies (RDBMS, NoSQL storage, triple stores, SQL, SPARQL/Cypher/Gremlin)
- Understanding of Big Data technologies (Hadoop, Spark)
- Knowledge of semantic technologies (RDF, OWL, SPARQL, etc)
- Proven experience in collaborative multidisciplinary teams.
- Ability to consistently deliver accurate effort estimates for work.
- Broad interest in scientific research and in the research process
- Willingness to acquire new skills and propose new approaches
- Participation in implementing large-scale global data warehousing and analytics projects
- Participation in open source projects (reporting bugs, proposing features and ideas and constructive criticism, providing bug-fixes and new implementations)
- Ability to apply the right database, data warehousing or data sciences technology for the right job
- Implementation and tuning experience using cloud computing platforms and frameworks
Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 16,500 students and 4,000 employees. Reflecting the university’s strong international profile, a fair amount of both students and staff are from abroad. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Humanities and Sciences, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience.
Institute of Data Science @UM
The Institute of Data Science (IDS) at Maastricht University is a recently established center led by distinguished professor Michel Dumontier. IDS is an interfaculty institute consisting of a core team of data science experts that cooperate closely with researchers across disciplines such as medicine, life sciences, social sciences and humanities, business and economics, knowledge engineering and smart services. The mission of the Institute of Data Science is to foster an interfaculty environment for collaborative innovation in the development and application of data science technologies.