Redux Experts in Munich

In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.

Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.

In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.

To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.

Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.

Achievements:

• Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
• Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
• Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
• Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
• Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
• Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
• Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
• Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
• Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
• Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
• Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
• Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
• Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
• Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.

Tech stack:

• Infrastructure as Code: Terraform, AWS CDK, Ansible.
• Containers: Kubernetes on EKS, AKS, Docker.
• Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
• Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
• Backend: Python with FastAPI, also Node.js with NestJS.
• Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
• CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
• Monitoring and Observability: Prometheus and Grafana.
• Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
• ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
• Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
• Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.

• Development of an accessible, responsive website based on Figma designs for a major sports event in compliance with WCAG, BITV, and EN 301 549
• Technical planning and setup of the tool stack with Vite, Playwright, Docker, and GitLab CI
• Worked on two existing projects (Vue.js & Nuxt), with a focus on bug fixing, accessibility audits, and new feature development
• Collaboration with design and QA to consistently implement accessible UI patterns
• Consulting on improving existing frontend architectures with regard to accessibility, performance, and maintainability

• Designed and developed a high-performance internal resource management platform using React and TypeScript, optimizing dynamic data rendering and state management.
• Built a React Native application to support mobile access to internal tools, enabling on-the-go project tracking for field teams.
• Developed custom 2D canvas-based visualizations using Pixi.js to simulate material flows and refractory layer behaviors.
• Integrated Pixi.js with React components for interactive diagrams and real-time UI updates.
• Developed interactive 3D visualizations using React.js for displaying refractory product layouts and simulations, supporting engineering and sales teams with dynamic product previews.
• Integrated Three.js within the React ecosystem to allow manipulation of 3D models in real-time via browser, enhancing user engagement and field configurability.
• Integrated a headless CMS to enable dynamic content updates by non-technical users, reducing content deployment time by 40%.
• Led AWS CloudFront optimization initiatives, improving portal load speeds by 30% globally.
• Actively collaborated with cross-functional Agile teams and product owners to deliver prioritized features with a fast feedback loop.
• Key Technologies: React.js, React Native, Three.js, TypeScript, Contentful CMS, AWS S3/Lambda/CloudFront, Cypress, Agile Scrum

• Participated in the Riyadh Public Transport (KAPT) project introducing public transportation in Riyadh, supporting multimodal journeys combining bus, metro, car on demand.
• Worked via Thiqah and partnered with Andersen to refactor the mobile MaaS application DARB into a new microservices architecture under SCRUM framework, successfully completing Phase 1.
• Facilitated a team of 40 developers across Android, iOS, Web, Java, DevOps, architecture, analysis, and QA.
• Moderated Scrum events and communicated with the client, proactively reporting on project deadlines, scope, and challenges.
• Coordinated cross-team efforts between Thiqah specialists and GPS development teams, managing Jira and Azure DevOps trackers.
• Supported and improved Scrum processes throughout the project.
• Due to Thiqah’s takeover by Elm, subsequent phases were handled entirely in Saudi Arabia with GPS providing IT consulting.
• Tools: Azure DevOps, Jira, Confluence, Draw.IO, ChatGPT

• Development of a test system
• Establishment of the architecture using Scala/Java for the backend and Swing for the frontend
• Setup of CI/CD pipelines with Jenkins for continuous integration, including automated builds
• Participation in the Scrum team, including daily stand-ups, sprint planning, and retrospectives
• Technology environment: Scala, Swing, EJB, JPA, Scrum, PostgreSQL, Mercurial

• Development of the ginlo Messenger client, emphasizing stringent data protection compliance as mandated by German legislation.

• The software supports the complete planning and approval of antennas for mobile telephony.

• The system was implemented using an event-driven microservice architecture for cloud-native deployment with Quarkus on the backend, Kafka for communication, and Angular for the frontend. Services run on Kubernetes in Google Cloud. A special challenge was synchronizing with the legacy system still used by some users.

• Fullstack development based on Next.js and TypeScript
• Development and optimization of geospatial database queries for PostgreSQL/PostGIS
• Visualization of geospatial data using Mapbox
• Design and execution of load tests and performance optimizations
• Code reviews and documentation tasks
• Technologies: JavaScript, TypeScript, Next.js, React, Zod, tRPC, Storybooks, PostgreSQL/PostGIS, MicroORM, Knex, Material UI, Mapbox, BullMQ, Jest, Playwright, Artillery.io, K6, Sentry, Figma, GitLab, Grafana, GTFS

• Did a complete rewrite of centrotherm.de with Next.js, migrating from Gatsby
• Built a system to automatically generate printable product datasheets
• Technologies: React, TypeScript, Storybook, SASS, Next.js, I18n

• Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
• Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
• Responsible for webinar:
• IoT edge computing: architecture, components, resources, management
• IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
• IoT processes, connectivity, data transfer and deployment, security
• Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
• Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
• Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
• Analysis of large sensor data sets with Apache Spark, Kafka clusters
• Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
• Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
• Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))