Software Engineer (Freelance)
As a freelancing Software Engineer, I support software development projects in Frontent, Backend or DevOps activities.
I enjoy to maximize the automation of software systems & development infrastuctures as well as the rapid but systematic development of software.
As a Startup Advisor, I support Startups, Scaleups, Grownups, Entrepreneurs and Freelancers in the setup, operations, and automation of their venture. Having co-founded Talentwunder (exited 2020) I accumulated knowledge on building, managing, and scaling Big Data Startups.
This knowledge enables me to assist in hiring, building and managing agile engineering teams, mentoring CTOs, monitoring continuous development and deployment activities, crafting and prioritizing successful product roadmaps, as well as designing and automating scalable tech architectures. On top, I help startups identify, validate and execute sustainable business models with technology.
Furthermore, I support startups in preparing a tech due diligence and advise investors to make smarter decisions by providing clear fact-based and business-oriented insights.
As a Researcher, I support Scientists, Research Groups and Startups in the setup, execution, evaluation of research endeavours as well as fundraising for research projects. My time at Fraunhofer IESE and SAP helped me learn critical success factors in planning, conducting, and analyzing empirical research in Software Engineering projects.
This experience enables me to assist Scientists and Marketers in executing empirical research by testing research and marketing hypotheses using tools such as surveys or experiments. Beside supporting empirical research I can help writing successful research project proposals for EU and German (BMBF, BMWi) research grants.
Previously, my background was rooted in the fields Empirical Software Engineering and Data Mining (applied to software-documents). In particular, I was interested in the diagnosis and refactoring of quality defects (Antipatterns, Code Smells, Design flaws, etc.), Experience-based / Pattern-based Software Engineering, Software Ontologies, Model-Driven Software Development (MDSD) as well as knowledge discovery and machine learning in code and defect repositories.