Highcharts Experts in Germany

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.

• Developed a web component for vehicle pricing communication in an international online shop.
• Replaced an old Stencil component to create a modern, flexible, and maintainable Vue.js component.
• Implemented country-specific displays and rules to comply with legal requirements.
• Introduced modal windows and tooltips to optimize user guidance.
• Adapted the component for desktop, tablet, and mobile devices.
• Provided different views and functions for stock and configurable vehicles as well as for private and business customers.
• Implemented efficient API calls and caching mechanisms to improve performance.

• Architecture and implementation of a fully digitalized closing flow for managing securities contracts within the DVAG infrastructure. The platform aims for maximum user-friendliness, modular extensibility and compliant handling of sensitive data.

• Implementation of a reactive UI structure with a focus on user guidance & accessibility.

• Dynamic control of form and closing processes including validation logic.

• Reactive state management via SignalStore (signals + selective effects).

• UX optimization through adaptive components and Playwright-based UI tests.

• Backend modularization to connect existing sales and contract logic.

• API stability and DTO design according to Clean Architecture principles.

• Collaboration with domain teams to define technical contracts and service boundaries.

• Management with GitHub.

• Unit tests with Jest, E2E tests with Playwright.

• Code reviews, CI-integrated test execution, iterative refactorings.

• Ensuring high coverage and UI stability in the closing flow.

• Technologies: Angular 18, RxJS, SignalStore, HTML5, SCSS, Spring Boot, Kotlin, REST, OAuth2, Jest, Playwright, Clean Architecture.

• Lead Product Owner for 4 development teams
• Leading and coordinating a greenfield project with parallel implementation of core components by independent teams; managing dependencies and resources
• Establishing a data lakehouse approach, including analysis of data volumes and future requirements as part of a cloud migration (best-of-breed approach)
• Responsible for requirements analysis, selection, and piloting of a LIMS/ELN system, supported by advising decision-makers and managing external vendors
• Introducing and managing an OpenWeb UI and Azure OpenAI-based RAG system to support knowledge extraction and data-driven analyses
• Setting up, configuring, and managing Jira projects, as well as developing project-specific workflows and automations
• Implementing classic Scrum processes with all ceremonies and taking on the Scrum Master role for all involved teams
• Assisting in hiring through interviews and assessments from a product owner's perspective
• Making key architectural decisions, including selecting the platform for the data lakehouse (Databricks) and the strategic integration of LIMS and analytics platforms

• Development of monitoring and control systems for public transport
• Development of a platform for real-time monitoring of passenger information and ticketing systems and an application for remote control and status monitoring of access systems
• Development of dynamic forms, complex tables, real-time dashboards (SignalR), and reusable components in the frontend (using Storybook)
• Implementation of REST interfaces in the backend
• Deployment to Microsoft Azure Cloud
• Technologies used: Angular 20, Angular Material 3, Dev Extreme, Storybook, C#/.NET 10

• 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

• Developed a reporting API with RESTful services and Spring Boot
• LDAP integration
• Migrated services to AWS
• Bootstrap
• Frontend development with Angular 14
• JBoss
• DynamoDB
• S3
• Athena

As part of an in-house development project, a pipeline for automated lead generation was created as the second stage of a preceding system for project monitoring. Based on a list of freelance project URLs (freelancermap), qualified lead records are generated, including company, contact person, official and personal email address, and the appropriate form of address (informal/formal). The technologies used were Python (openpyxl, requests) as well as an LLM agent workflow in VS Code (GitHub Copilot Chat) with a custom slash command and extensive rule set; search APIs (Serper.dev) are connected for research. My tasks included the full concept and development. The core is a rule set of around 480 lines that guides the LLM agent deterministically through extraction, website and email lookup, duplicate matching (against existing provider lists), and the creation of a structured JSON output. Deterministic steps (Excel matching, web/email search, pattern derivation) were moved into Python helper scripts. Other requirements included validating the results through versioned blind runs against a reference, iterative refinement of the rule set, and a swappable search provider layer for cost and stability reasons.