Virtualization Experts in Munich

• Leading enterprise BI transformation across Power BI & Microsoft Fabric, delivering scalable data models, automated reporting, and high-performance analytics solutions for commercial and operational leadership.
• Building and optimizing Power BI Dataflows, Fabric Lakehouse datasets, semantic models, and automated reporting pipelines to improve data scalability, governance, and reporting performance.
• Driving dashboard modernization and KPI governance by translating complex business requirements into executive-level insights, interactive visualizations, and decision-ready analytics.
• Designing end-to-end Microsoft Fabric architectures integrating data ingestion, transformation, virtualization, and enterprise reporting across cross-functional business domains with SAP BW to Qlik to Power BI migration.
• Delivering AI-enabled reporting capabilities, threshold-based alerting, and automation frameworks within the Power BI ecosystem to accelerate business decision-making.
• Partnering with commercial leadership, analytics teams, and IT stakeholders to standardize KPIs, optimize BI strategy, and deliver scalable, business-critical reporting solutions.
• Recognized for combining strong stakeholder leadership, technical architecture expertise, and business-driven analytics to deliver impactful enterprise BI transformation initiatives.

Unified ITSM framework

• Definition of a company-wide ITSM target picture
• Introduction of a uniform service structure across all business units

SLA and OLA management

• Building a standardized SLA framework
• Definition of service classes (Business Critical, Standard, Low Priority)
• Introduction of OLAs between internal teams
• Building meaningful SLA reporting
• Definition of KPI and service dashboards for business units

Service portfolio management

• Definition of service descriptions
• If needed, preparing possible cost and service billing

Ticketing & processes

• Incident management
• Uniform ticket categories
• Standardized prioritization
• Escalation matrix
• Automations
• Self-service optimization

Request fulfillment

• Service catalog across all business units
• Approval workflows

Problem management

• Introduction of root cause analysis
• Known error database
• Problem review process

Complete asset management concept

• Hardware lifecycle management
• Software lifecycle management
• Leasing lifecycle
• Mobile device lifecycle
• Monitor lifecycle
• Phone lifecycle

Processes

• Procurement
• Goods receipt
• Inventory
• Assignment
• Return
• Disposal
• Leasing return Goal: single source of truth for all assets

CMDB design

• Definition of all configuration items:
• Workplace
• Notebooks
• Monitors
• Mobile phones
• Printers

Infrastructure

• Servers
• Firewalls
• Switches
• WLAN
• Storage
• Backup systems

Cloud

• Azure resources
• Microsoft 365
• SaaS services

Relationships

• User ↔ Asset
• Asset ↔ Service
• Service ↔ Infrastructure
• Location ↔ Asset
• Goal: make all service dependencies visible

Software asset & license management

• License management concept
• License balancing
• Compliance reporting
• Microsoft license management
• Adobe license management
• SaaS management
• Contract management
• Renewal management

Interfaces & automation Existing systems

• Workday
• Joiner
• Mover
• Leaver

TESMA

• Leasing data
• Contract data

Matrix42

• Asset synchronization
• User synchronization

Active Directory / Entra ID

• User management

Microsoft 365

• License assignment
• Group management

Dormakaba

• Access processes

• Lifecycle services

Monitoring platforms

• PRTG
• Palo Alto
• Cisco

Reporting & KPI framework

• Definition of a management dashboard
• KPIs
• Ticket volume
• SLA fulfillment
• MTTR
• First resolution rate
• Asset accuracy
• License compliance
• Change success rate
• Service availability
• Degree of automation

Network redesign support

• Governance
• Support of the network redesign from an ITSM point of view
• Definition of affected services
• Change management structure
• Communication concept

CMDB integration

• Recording of all network components
• Service mapping
• Dependency analysis

Validation of documentation and knowledge base articles

• Network documentation
• Operations documentation
• Standard changes

Monitoring & event management

• Target picture
• Central monitoring concept
• Event management process
• Alerting strategy
• Escalation model

Systems

• Cisco

• Palo Alto

• Fortinet

• Rubrik

• Veeam

• Matrix42

• Azure

• Microsoft 365 Automation

• Ticket creation from monitoring

• Escalations

• Standard actions

Audit, compliance & information security

• ISO 27001 consulting
• TISAX consulting
• NIS2 preparation - consulting
• Audit-ready processes
• Documentation structure
• Evidence tracking in Matrix42

Roadmap

• 12-month roadmap
• Prioritization of all measures
• Quick wins
• Medium-term projects
• Long-term target picture
• Documentation

Oracle Project Management and Automation

As part of the project, the operation of an Oracle infrastructure was automated by developing and enhancing Puppet modules and migrating existing Bash scripts to a standardized configuration management solution.

The aim of the project was to standardize and automate administrative processes, improve the maintainability of the Oracle platform, and increase the efficiency and traceability of infrastructure management through the use of Infrastructure as Code.

• Analysis and migration of existing Bash shell scripts to a Puppet-based automation solution
• Development and enhancement of Puppet modules for Oracle environments
• Automation of installation, configuration and operational processes using Infrastructure as Code
• Standardization and optimization of configuration management for Oracle systems
• Creation and maintenance of technical and operational documentation
• Planning, coordination and professional support during project implementation
• Operation and automation of Oracle systems on x86-based Linux server platforms
• Testing and quality assurance of the developed Puppet modules
• Analysis and resolution of technical errors as part of the automation and migration
• Close coordination with specialist departments and infrastructure teams to implement automation requirements

Technologies used: Puppet 7/8, SLES 15, Oracle 19, Oracle Grid, SVN, Windows Server, Confluence, VS Code, Ruby, Bash, Pass

• Analysis and optimization of the VMware environment for operation in a KRITIS environment
• Planning and implementation of updates to the VMware and hardware environment in a KRITIS environment
• Set up Skyline Health Diagnostics
• Review of the existing documentation
• Training and onboarding of new internal employees
• Support with migration and upgrade projects
• Preparations for moving scripts in the virtualization environment to GitLab
• Ticket processing with Service.Now

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.

• Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
• Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
• Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
• Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
• Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
• Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
• Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
• Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
• Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
• Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
• Integration of RESTHeart to create a REST API for MongoDB.
• Build an Angular frontend to simplify data queries and master data maintenance.
• Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
• Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

Schaeffler: SASE Harmonization (SD-WAN/Zscaler Client Rollout)

• Overall project planning (including planning, preparation, and execution of the migrations)
• Project Management Office (PMO) – preparing status reports, monitoring progress with dashboards, preparing and conducting steering boards
• Coordination with the regions and departments to prepare the migrations
• Coordinating the execution of the migrations (cut-over)
• Coordination with the managed service provider for SD-WAN

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
• Development of feature store and data synchronization between environments
• Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
• Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
• Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
• Development of Copilot Agent Skills

• Responsible for Openshift/Kubernetes on-prem administration and developer support.
• Developed URP infrastructure automation with Python, Ansible, Kustomize and ArgoCD, Argo Workflow/Events stack.
• Wrote smoke and load tests for URP infrastructure utilizing Python, Kustomize and ApplicationSets.
• Helped to set up and deploy URP infrastructure in Google Cloud, GKE.
• Set up monitoring for URP and ArgoCD stack with Splunk Cloud.
• Performed system administration tasks across RedHat Linux, Kubernetes/Openshift, ArgoCD, GitLab, Bitbucket Enterprise, Kafka and MongoDB.

• Lead Architecture in an interdisciplinary architecture team covering enterprise, solution, NOOTS, cloud and security architecture as well as regulatory and European interoperability requirements.

• Public / Government

• Development and management of target architectures, principles, standards and technical guardrails; assessment of Security-by-Design, IAM, data protection and interface/integration architectures; architecture reviews, security assessments, risk analyses, and preparation of decision records, target visions, roadmaps and decision papers. Architectural knowledge and consideration of relevant AD-NOOTS requirements, particularly for IAM, authorization, secure integration and interoperability.

• Technologies / Methods: Enterprise Architecture, Solution Architecture, Security Architecture, Cloud Architecture, NOOTS / EU-OOTS, IAM, Authorization, Security-by-Design, API/Integration Architecture, Interoperability, Risk Analysis, Architecture Governance

• Security and integration requirements as well as technical decisions are structured in reliable architecture and decision artifacts.

• Spearheaded the EU market entry strategy for L3/L4 autonomous logistics vehicles, driving the technological localization and deployment of the parent company’s smart robotics portfolio.
• Orchestrated technical alignment between top-tier autonomous driving suppliers across China and Europe, translating complex client requirements into precise engineering specifications compliant with EU standards.
• Cultivated strategic joint R&D initiatives with leading European universities, research institutes, and enterprises, accelerating the transition of cutting-edge robotic concepts into commercial products.
• Directed the end-to-end architecture of intelligent warehousing solutions, guiding cross-functional teams in optimizing hardware integration for autonomous vehicles & robots, and overall system performance.
• Led the R&D of high-fidelity simulation and AI algorithms using NVIDIA Isaac Sim & Lab, establishing robust "Sim-to-Real" pipelines to train and validate dynamic path planning optimization, intelligent obstacle avoidance, and complex navigation stacks prior to physical deployment.
• Maintained hands-on oversight of the core system architecture, focusing on bottom-level performance tuning, AI model inference acceleration with TensorRT/ONNX Runtime, and sensor integration.

• Specialized in designing and implementing robust, secure cloud solutions for critical client infrastructure.
• Expertise in Microsoft Intune environment with a strong focus on system hardening and comprehensive policy management.
• Architected NIST and ISO/IEC 27000 compliant Mobile Device Management (MDM) infrastructure tailored for an international government defense aerospace project.
• Performed an architectural role for an offline Microsoft Endpoint Configuration Manager (MECM) environment, ensuring NIST compliance while handling complex manufacturing infrastructure.

• Designed and implemented Azure Kubernetes Clusters, and managed DNS and Firewall solutions, enhancing network security and reliability.
• Led projects using Agile Scrum methodologies to streamline development cycles and improve project efficiency.
• Fostered and maintained relationships with suppliers to ensure timely project deliverables and resource availability.
• Managed continuous integration and delivery (CI/CD) pipelines using Jenkins, Sonar, GitHub, Bitbucket, and Terraform within the BMW Azure Cloud environment.
• Utilized Fortify SSC and Contrast AST for robust application security testing.
• Directed DevOps engineering initiatives on the SAP Business Technology Platform (BTP), focusing on streamlining development and deployment processes.
• Service Now governance, risk und compliance (GRC&IRM).