Mockito Experts

I designed and built a self-service AWS platform for Data Science teams with little or no infrastructure-as-code experience. A team writes about 10 lines of Python and gets a deployable stack with full SDLC release cycle that already follows the governance rules. Networking, encryption, IAM and tagging are decided by the platform, not by each team.

New teams or AWS accounts are onboarded with one pull request. Waste prevention and cost control is built into the defaults: Data scientists don't need infrastructure skills.

• Construct library (AWS CDK, Python). Built a versioned catalog of about 20 hardened constructs for being used in data-science projects with ML context
• Governance as code
• Multi-account, multi-tenant
• Golden-path scaffolding
• CI/CD and supply chain. Built reusable GitHub Actions workflows
• Release engineering
• QA with e2e, contract testing and unit test coverage with each release candidate

• Analysis of existing microservice structures and integration into the event-driven architecture
• Design and implementation of prototypes
• Introduction of structured workflows and integration into a solid testing strategy
• Team: Small specialized team (3-5 people) from analysis through implementation readiness
• Documentation of architectural decisions and definition of interfaces
• Integration of the event-driven pipeline into the existing landscape
• Technologies: Kafka, Docker, Microservices, Event-Driven Architecture, Java, Liquibase

• Planned and developed a CPU monitoring application for monitoring system performance and resource utilization.
• Designed and implemented a Spring Boot backend providing a REST API for processing and exposing monitoring data.
• Developed the React frontend for presenting monitoring information in a clear and user-friendly interface.
• Integrated PostgreSQL for persistent storage and management of application data.
• Containerized the application and its services using Docker Compose.
• Automated infrastructure provisioning and deployment using Terraform on AWS.
• Structured the application as a modern, maintainable system using REST-based communication between frontend and backend.
• Designed and developed a secure, scalable CPU monitoring architecture (cpu-watcher) with a dedicated collector application that streams monitoring data to the backend, reducing direct exposure of system resources.
• Designed a secure cloud infrastructure with the database isolated within a private network and OIDC-based authentication.
• Implemented Infrastructure as Code with Terraform and integrated version-controlled CI/CD pipelines to automate testing, infrastructure changes, and application deployments.
• Designed and implemented the frontend delivery architecture using AWS CloudFront.

Stack: Spring Boot · React · PostgreSQL · REST API · Docker Compose · Terraform · AWS

Maturing their API practices on both a business and technology level. My role covers strategy, architecture, developer advocacy as well as hands-on software engineering, enabling both technical teams and business leadership to adopt and act on API-centric principles effectively. Coincidentally, we also establish GitOps, DX and platform best practices with this project.

Highlights:

• Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
• Formulating a way forward for API Lifecycle Management at OTTO
• Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals

API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, React, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.

• Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
• GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
• AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner

• Designed a migration strategy for WLS applications to BKA Cloud and Stack IT

• Created a hybrid cloud for internal testing and development with Kubernetes

• Transformed the IT landscape from an outdated monolithic architecture to a cloud-based architecture

• Integrated new CI/CD processes using Azure Pipelines

• Quadrupled the processing throughput for personnel security checks

• Migrated personnel security screening applications to Docker-based applications

• Implemented CI/deployment pipelines using Azure DevOps

• Environment: Java Springboot, Kubernetes, Oracle Weblogic, OpenID, Camunda, JUnit, TOGAF

• Recurring problem in my own AI-assisted projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but remain difficult to follow and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions form a consistently linked knowledge graph, traceable from requirement to architecture decision – queryable by both people and AI agents. Technically based on RDF/OWL and a custom MCP server.

• Result: Working MCP daemon, Docker image published automatically to GHCR, nine hexagonal modules, eleven ADRs (including an Open-Core licensing model). Requirements engineering and Ubiquitous Language hexagons are active. Public as a Community Edition under Apache-2.0 since 07/2026 (github.com/kogn-io/arknet), together with the Claude Code plugin and GHCR image; Open-Core model.

• Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ, Interface Development, Software Architecture, Continuous Integration, Knowledge Management

Development and further development of an internal platform for managing and providing technical resources, virtual machines, and infrastructure services. The platform supports self-service processes and covers functions that are conceptually comparable to cloud management solutions like Azure or AWS.

• Responsible involvement in the design, development, and implementation of new backend and frontend features
• Hands-on development with Java, Spring Boot, Python, and Angular
• Implementation of REST interfaces, business logic, validations, and integrations into existing system landscapes
• Further development of modern web interfaces with Angular, including connection to backend services
• Participation in architecture and design decisions within the team, especially with regard to scalability, maintainability, and clean interfaces
• Containerization and deployment of applications with Docker, Kubernetes, and Helm
• Support with CI/CD processes and deployment to Kubernetes-based environments
• Work in the environment of vSphere, Broadcom, GitLab CI/CD, ArgoCD, Maven, npm, and NuGet
• Close collaboration with developers, business teams, DevOps, and other technical stakeholders
• Analysis of technical requirements, deriving suitable solutions, and independent implementation in an agile team
• Use of GitHub Copilot to support code generation, refactoring, test case creation, and technical documentation

Methods/ tools/ technologies: Languages & frameworks: Java (21), Spring Boot (4.x), Python, Angular, Robot Framework, Kubernetes, Helm Persistence: PostgreSQL, MongoDB, Hibernate, Liquibase Architecture & communication: REST, gRPC, GraphQL, Apache Kafka, OpenAPI, Microservices, Event Driven, Domain Driven Design Cloud & infrastructure: Terraform, Docker, Rancher, Helm, Ansible Security: OAuth2, MS (Entra ID), web security, Keycloak (extensions for detailed group rights) DevOps: GitLab CI/CD, Ansible, Maven, Gradle, Grafana, Prometheus, Git, GitHub Copilot Testing & QM: JUnit, Robot Framework, automated component and integration tests, E2E tests with Playwright, Testcontainers, EasyMock Methodology & approach: Kanban, JIRA, Confluence, Clean Code

• Modernization of a large-scale Java enterprise application*

The project is modernizing a complex enterprise application that has grown over many years. The existing Spring-based legacy system runs on Java 8, OSGi, and Eclipse RCP and is being gradually migrated to a modern, maintainable architecture with Java 25 and Quarkus.

Marcus works on analysis, architecture, refactoring, and implementation. One focus is on untangling historically grown structures and dependencies and on building a clean, sustainable Java and Quarkus technology stack.

Tools & technologies: Java 8, Java 25, Quarkus, Hibernate ORM with Panache, EclipseLink, OSGi, Eclipse RCP, Maven, JUnit, Mockito, REST, JSON, Git, Eclipse IDE, IntelliJ IDEA Ultimate, Jira, Confluence

• Implementing and extending an Electron app based on TypeScript, React and Node.js to display stream content and technical data
• Extending a Kotlin backend for message processing via Kafka
• Extending various React modules
• Programming custom solutions for customers, including testing
• Creating documentation
• Development in accordance with Scrum processes

Used skills: React, React Hooks, Jest, Vitest, Typescript, Electron, IntelliJ, Vite, Github, Jira, Node.js, yarn, npm, pnpm, Swagger, OpenAPI, Storybook, Docker, Prettier, Eslint, tailwindcss, i18n, GraphQL, CoPilot, Postgres, JSON, Sqlite, trpc, influxdb, Java, Spring, Kotlin, Kafka, Git, Sonarcube

• Building a data integration platform for high, medium, and low voltage assets for contextual analysis of time series with master data from the SCADA control system (IEC 60870 104), INIS, and SAP.
• Responsibility for the architecture and implementation of the solution, as well as sparring partner for the Product Owner.
• Use of Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter, and various API gateways.

• Optimizing and accelera­tion of our Gitlab CI pipeline

• Conceptual work for the PoC of the Zero Trust system

• Extension of the policy-engine backend in Go

• Extension of the policy-testing mechanism in Python

• Architectural design of the PEP component of Zero Trust

• Documentation of the product

• Technologies: Zero Trust, Go, Python, Gitlab CI, Docker, JWT, Domain-Driven Design

• Ride-sharing and bus platform, live and fully functional. Backend with Spring Boot 4.1 on Java 21, PostgreSQL with Flyway, and Testcontainers integration tests. Hosted in my own AWS account (ECS Fargate, ALB, ECR, IAM Least-Privilege) with CI/CD via GitHub Actions and OIDC federation without static credentials. Development fully AI-supported with Claude Code, including custom skills and project-specific memory. Spring Boot · Java 21 · PostgreSQL · Flyway · Docker · AWS ECS/ALB/ECR · CI/CD · GitHub Actions · Claude Code

• Client: Prime Force Group GmbH

Technologies used: Java 25, Spring Boot 4, MapStruct, JSpecify, PostgreSQL, Redis, Liquibase, REST/OpenAPI, Apache Kafka, Apache Solr, OpenID Connect via IronGate/Keycloak, SAP Customer Data Cloud, JUnit, Testcontainers, Karate, Playwright, GitLab monorepo with CI/CD, Jenkins, JFrog Artifactory, FluxCD, Docker, Kubernetes on Azure, OpenTelemetry, arc42, Jira, Confluence

The Team Management Center is the new central platform of the DFB for planning, managing, and carrying out team activities for the national teams - from squad selection and training camps to communication with players, clubs, and legal guardians. The platform is designed for multi-tenancy for the DFB and regional associations; player, club, and master data are intentionally not copied, but connected at runtime via the DFBnet APIs.

I have been involved in the project continuously since the architecture and concept phase (Sprint 0) and work in a distributed Scrum team in two-week sprints. In addition to implementation, my focus is on architecture alignment, connecting the DFBnet interfaces, as well as code reviews and test automation as quality assurance in the team.

Focus areas:

• Development and implementation of the multi-tenancy concept (tenant model for the DFB and regional associations), including data model, access layer, and Liquibase migrations.
• Design of the person service and the search concept based on Apache Solr.
• Integration of the DFBnet APIs (player, person, and club search, club data), including authentication and synchronous master data synchronization.
• Hardening the integration through resilience patterns: separate read timeouts for each search path, correction of circuit breaker counting, limiting parallel requests, and a club cache to reduce load on the external system.
• Development of self-service endpoints for players (own activities, activity details, games), including an access concept for participants, as well as person documents and file uploads.
• Standardization of API design: OpenAPI annotations, nullability model via a custom ModelConverter, JSpecify migration of the DTOs, and documented API guidelines.
• Build and maintenance of Karate-based API and integration tests, integration tests with Testcontainers, test guidelines, and bug triage from the integration and reference environments.
• Code reviews via merge requests, architecture documentation according to arc42, and architecture decisions (ADRs) in Confluence.
• Automated deployment to the integration and reference environments, analysis of login and OIDC issues in combination with IronGate.

Status: ongoing - as of 08/2026 in Sprint 17, around 940 person hours worked; testable delivery to the integration environment every two weeks.