Oliver Fries

Technical leadership and stabilization of an analytical optimization platform focusing on reliable delivery, scalability, and quality for enterprise clients.

Key Contributions & Results

Led a 6-person development team over five releases, increasing sprint delivery rate from approx. 70% to over 95% through consistent prioritization of technical debt.

Established a new CI/CD pipeline (TeamCity, Docker) and reduced build and deployment times by 40% through standardized stages and automated gates.

Accelerated release cycles from quarterly to bi-weekly, enabling significantly faster feature delivery for enterprise clients.

Reduced production errors by 64% within six months through test-driven architecture in five core modules and consistent expansion of automated tests.

Increased runtime performance of the optimization platform by up to 40% through caching strategies, decoupling critical paths, and stabilizing computationally intensive processes.

Consolidated redundant logic in three subsystems and halved the onboarding time for new developers from four to two weeks through clear module boundaries.

Technical Metrics

Build & Deployment Duration: Reduction by 40% (25 → 15 minutes per run).

Release Cycle: Transition from quarterly to bi-weekly within half a year.

Automated Test Coverage: Expansion from 46% to 87% in five business-critical modules.

Production Errors: Decrease by 64% within six months after architecture and testing measures.

Technologies & Methodologies

C#, .NET, Blazor, Entity Framework Core, Docker, TeamCity, NUnit, Shouldly, Moq, CI/CD, Test-Driven Development, Code Reviews, Software Architecture, Performance Optimization, Agile Scrum

Development, modernization, and stabilization of industrial software and research prototypes with a focus on AAS, CI/CD, system stability, and automated generation.

Key Contributions & Results

Developed a GenAI prototype for automated AAS creation from technical documents and nameplates, reducing manual modeling effort from approx. 5 hours to under 15 minutes per AAS.

Restructured the AASX Server and PackageExplorer for productive use through clear architectural decisions for six core components and reduced critical runtime errors by approximately 70%.

Established a continuous CI/CD pipeline with GitHub Actions and shortened release cycles from several weeks to under seven days through automated builds, tests, and analyses.

Measurably increased system performance and reduced startup times by 35% through modernization of runtime dependencies and optimization of build pipelines.

Sustainably improved code quality through binding coding standards and automated tests, significantly reducing rework due to regressions.

Enabled project-wide reusability through definition of interoperable AAS and OPC UA data models in industrial projects like Factory-X and Condition Monitoring.

Engineering Metrics

AAS Creation Effort: Reduction from approx. 5 hours to <15 minutes per model.

Critical Runtime Errors: Decrease by approx. 70% after architecture and containerization measures.

Release Cycle: Shortening from several weeks to under 7 days.

Test Coverage: Expansion from under 10% to approx. 45% across 33 modules.

Technologies & Methodologies

C#, .NET 8, Blazor, Python, Docker, GitHub Actions, AAS, AASX Server, OPC UA, CI/CD, Automated Testing, Software Architecture, Performance Optimization

Mentoring a junior developer from training to professional entry, focusing on structured development and practical application.

Key Contributions & Results

Supported an aspiring software developer for over 12 months until successful completion of training and clear professional entry.

Established a binding learning structure with weekly learning goals and regular mentoring sessions for sustainable skill development.

Accelerated the practical application of technical fundamentals through real task assignments, significantly shortening the ramp-up time for new topics.

Supported professional entry through technical preparation and decision-making guidance, avoiding trial-and-error changes.

Engineering Metrics

Mentoring Duration: >24 months of continuous support.

Learning Structure: Weekly learning goals throughout the final year of training.

Practical Application: Reduced onboarding time from several weeks to a few days.

Technologies & Methodologies

Software Development, Mentoring, Structured Knowledge Transfer, Problem Analysis, Feedback Processes