Containerization Experts in Cologne

• Built more than 15 frontend applications and more than 10 backend services across internal tools, back-office workflows, and external portals for installers and end customers
• Automate build and release workflows with TypeScript and Azure DevOps
• Mentor junior engineers through code reviews and technical guidance
• Integrate AI-assisted and agentic development practices into engineering workflows
• Coordinate user experience decisions with UX designers

Tech Stack: Claude Code, Codex, AWS (S3, Lambda, AppSync, Resolver), Next.js App Router, ReactJS, NuxtJS, Vue.js, Storybook, JavaScript, TypeScript, GraphQL, Azure DevOps

• Regulated environment within a German banking group (approx. 8,500 employees, hybrid cloud strategy).

• Responsible for operating, provisioning, and continuously securing business-critical platforms – including a GenAI chat application, a big data/AI platform, and data science workspaces based on Azure Virtual Desktops and VMs. Ownership of Azure DevOps projects for ShaiHulud and React2Shell, as well as BSI alerts – Security Operations improvements across the SDLC.

• Deployment responsibility for the GenAI chat application, big data/AI platform (BDAI), and data science workspaces (AVD/VM-based) in the respective landing zones.

• Deployment & release management: end-to-end responsibility for deploying portal and service applications across multiple Azure landing zones, including technical approvals, compliance with development team deployment guidelines, and ensuring ITIL-based change and release processes via ServiceNow.

• Azure landing zones & network architecture: design, provisioning, and operation of Azure landing zones for 3-tier web applications with enhanced network segmentation, VNet peering, hub-and-spoke architectures, private endpoints, and firewall integration across separate subscriptions and tenants.

• Azure DevOps governance & operations: ownership of the Azure DevOps organization, including projects, repositories, and CI/CD pipelines; implementation of governance requirements such as branch policies, approval gates, permission models, and audit-ready operating structures.

• Infrastructure as Code (Terraform): design, implementation, and operation of a modular Terraform architecture for standardized cloud infrastructure deployment, including state management, provider versioning, reusability, and policy-as-code approaches.

• CI/CD pipeline engineering: design, operation, and optimization of complex YAML-based CI/CD pipelines with multi-stage deployments, template standardization, self-hosted agents, integrated secret management, and automated quality and security checks.

• Git migration & platform consolidation: planning and execution of repository and pipeline migration from Azure DevOps to GitLab CI/CD, including automated scripts, full Git history transfer, pipeline porting, and platform consolidation.

• Container & platform operations (AKS): operation and security assessment of containerized workloads on Azure Kubernetes Service, centralization of on-premises container registries for ACR.

• OpenShift (OCP) security reviews: security assessment of code baselines, build pipelines, and deployment processes for on-premises OpenShift clusters with critical applications, and derivation of specific hardening recommendations.

• Shift-left security & DevSecOps transformation: introduction of a company-wide shift-left approach for early security integration in development and deployment processes, enabling developers to perform self-led security checks and sustainably reduce vulnerabilities before production (IDE integrations, pre-commit hooks, local scanners).

• Software supply chain security: analysis and mitigation of supply chain risks in NPM- and Yarn-based applications through dependency audits, CI/CD pipeline hardening, token rotation, and restriction of risky build and lifecycle mechanisms.

• Frontend & framework security (React / Next.js): security assessment and coordination of critical vulnerability remediation across platform applications and web frameworks, including coordination and complementary technical mitigations with all teams following BSI alerts.

• Software composition analysis (SCA): introduction and operation of automated vulnerability scans for container images, pipelines/artifacts, and third-party dependencies, including SBOM exports within CI/CD pipelines.

• SAST/DAST integration: design and piloting of static and dynamic application security tests in close collaboration with security architecture and development teams, for continuous improvement of code and runtime security, and establishing operational acceptance tests.

• Artifact & registry consolidation: analysis and consolidation of all package and container repositories for service applications and AKS workloads, aiming for a centralized, secured registry strategy with centralized vulnerability scanning and governance.

• Dependency-Track & SBOM strategy: advising the compliance board on introducing a central SBOM and vulnerability management platform to increase enterprise-wide dependency transparency and accelerate CVE response capability.

• CI/CD pipeline hardening: security analysis and cleanup of the existing pipeline landscape by removing unused pipelines, improving secrets hygiene, implementing least-privilege principles, and isolating build agent environments.

• Azure Web Application Firewall (WAF) optimization: analysis and tuning of existing Azure WAF rules (OWASP Top 10 Core Rule Set, DSR/SDC, custom rules) to defend against known vulnerabilities and exploit patterns, including reducing false positives and improving threat detection.

• Documentation & stakeholder communication: creating and maintaining technical documentation, runbooks, and architecture overviews in Jira and Confluence, as well as active knowledge transfer between operations, development, security, and compliance stakeholders.

• Overall responsibility for platform transformation across ERP, CRM, PIM, and AI-driven process automation
• ERP transformation: Implementing and migrating to Microsoft Dynamics 365 Business Central
• CRM & growth stack: Implementing HubSpot as the central CRM and a platform for lead and marketing automation
• Product data & commerce enablement: Introducing an ePIM to scale category management and digital commerce processes
• AI-driven process automation: Designing and implementing agent-based AI workflows to automate processes in business services, accounting, tender management, and marketing
• Building a scalable platform organization including product development, DevOps, UX, cloud engineering, and corporate IT
• Implementing modern product and portfolio governance (e.g., OKRs, portfolio boards, value-based roadmaps) to manage technology-driven initiatives
• Harmonizing heterogeneous system landscapes and transforming them into a modular, scalable platform architecture (ERP, CRM, PIM, integrated customer journeys)
• Coaching and developing product owners, tech leads, delivery managers, and UX teams to build a scalable product organization

• AWS
• Genedata Profiler
• Data Lake
• APIs
• Rstudio
• GitLab
• Python
• R
• Data acquisition, integration, and simulation
• Multiplex immunofluorescence
• Copy-number variation calling
• HLA typing and loss-of-heterozygosity analysis
• RNA expression analysis

• Developed a SCIM-compliant Python interface to connect external systems
• Expanded and enhanced UCS modules in Python
• Extended and newly developed Helm charts for container orchestration in Kubernetes
• Maintained and further developed CI/CD pipelines in GitLab
• Implemented automated tests with Pytest and Playwright

'Nubus' is based on a microservice architecture, relies on open source technologies, and runs in Docker containers.

UCS is an IAM complete solution based on Debian, used as an alternative to Microsoft's Active Directory. The work was characterized by the close integration of infrastructure and application logic.

• Evolved the platform from bare-metal infrastructure with a monolithic PHP application to a hybrid Google Cloud architecture with Go microservices on Kubernetes
• Managed and scaled a production infrastructure with 300+ VMs and dozens of clusters, including MySQL, PostgreSQL, MongoDB, RabbitMQ, Redis and Elasticsearch clusters
• Designed architecture, deployment, monitoring, performance analysis, and upgrades for all platform services using Terraform and Ansible
• Migrated observability systems from ELK and Prometheus to Datadog, managed with Terraform
• Introduced Jenkins CI/CD with SonarQube integration and automated tests to speed up feedback cycles
• Containerized the testing environment and implemented GitLab CI pipelines for Docker image builds, static code analysis, and infrastructure tests
• Performed zero-downtime migrations of MySQL and MongoDB clusters to Google Cloud
• Developed reusable Ansible roles for database clusters and automated data obfuscation for staging environments
• Decomposed monolithic databases and migrated to microservice architectures
• Built tools to detect and optimize slow queries in MySQL and MongoDB
• Conducted online schema migrations with pt-online-schema-change without downtime windows
• Developed internal Go applications for batch queries, GitLab-Jira integration, and MongoDB backup recovery
• Technologies: Debian, Ubuntu, Alpine, Go, Bash, Python, PHP, SQL, GitLab CI, Jenkins, Drone CI, MySQL, MongoDB, PostgreSQL, Redis, Elasticsearch, RabbitMQ, Kafka, Docker, Kubernetes, Nomad, Ansible, Terraform, Grafana, ELK, Prometheus, Datadog, Nagios, Zabbix, Nginx, HAProxy, Vault, Helm, SonarQube, Filebeat, Auditbeat, Google Cloud, AWS

• Independently initiated and deployed automated validation workflows using Python, cutting manual processing by 58% and improving efficiency
• Developed a machine learning model for synthetic defect generation, reducing downtime and production costs; deployed locally and via Databricks and Azure AI Factory
• Utilized a small dataset of image data from the production lines and extended this dataset with training on models like cycleGAN and pix2pix
• Built and optimized the Linux-based development environment for training 3D models; maintained reproducibility via GitHub
• Presented technical insights to cross-functional teams (engineers, QA, project managers), ensuring alignment of ML solutions with operational needs

• Developed automated ETL pipelines with AWS Glue and Athena to ensure consistent data quality and governance requirements
• Implemented validation, anonymization, and encryption measures for data in compliance with GDPR
• Optimized cloud costs by introducing FinOps practices and increased transparency for business units
• Monitored performance, performed root cause analyses, and ensured adherence to SLAs
• Supported data and solution architects in building scalable data models for ML and analytics scenarios