
Multi-Cloud Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Multi-Cloud
Alexandru G.
Last position:
Principal Cloud DevOps Architect at BP
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.
Thomas H.
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- 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).
Hardeep B.
Last position:
Sr. Data Engineer at Charles Schwab Bank
- Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
- Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
- Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
- Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
- Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
- Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
- Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
- Created and maintained dashboards in Power BI to provide actionable insights.
Marc E.
Last position:
Agile Transformation and Team Development Lead - DevOps & Cloud Adoption at IT Business Unit of Leading European OEM
- Guided a large-scale agile transformation, improving team agility, DevOps adoption, and cloud readiness.
- Supported DevOps & CI/CD practices to optimize cloud deployment strategies and reduce time-to-market.
- Led agile coaching initiatives to enhance cross-functional collaboration between IT and business teams.
- designed agile governance structures to align executive leadership with cloud and digital transformation goals.
- Technologies & Frameworks: Agile (Scrum, SAFe), DevOps, Cloud Adoption, IT Governance, CI/CD, Change Management
Tobias N.
Last position:
Enterprise & Solutions Architect
- Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
- Migration of all applications; avoiding high contractual penalties for the client.
- Onboarding and coordination o...
Tezcan D.
Last position:
Solution Architect / Project Manager at German Football Association
- Overall responsibility for the project lifecycle from scope definition to completion
- Close collaboration with platform teams, IT leaders, and external service providers
- Application of SAFe principles and structured sprint work
- Creation of a migration roadmap with clear milestones
- Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
- Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
- Regular status reports and running knowledge transfer sessions
Discover over 15,000 top freelancers
Statistics of experts using Multi-Cloud
Aggregated from the professional profiles of matched freelancers.
Experience
23 years (Germany: 20 years)

Position duration
2.2 years (Germany: 1.9 years)

Positions per freelancer
13 (Germany: 14)

Top business areas
Information Technology, Operations, Project Management

Top industries
Information Technology, Automotive, Telecommunication

Certification focus areas
Information Technology, Accounting, Finance
Bachelor's degree or higher
100% (Germany: 87%)
Master's degree or higher
80% (Germany: 45%)
Doctorate
20% (Germany: 4%)

Certifications per freelancer
3 (Germany: 6)

Most common languages
English, German, Romanian

Speak two or more languages
83% (Germany: 98%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Multi-Cloud
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Multi-Cloud experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Automotive (67%)
- Telecommunication (67%)
- Energy (50%)
- Banking and Finance (50%)
- Transportation (50%)
- Manufacturing (50%)
- Retail (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Designing Resilient Cloud Architectures
Modern enterprises distribute workloads across multiple public hyperscalers to prevent single-vendor lock-in, ensure business continuity, and meet stringent compliance mandates. Multi-cloud architectures balance critical services across Amazon Web Services, Microsoft Azure, and Google Cloud Platform while maintaining operational efficiency.
Unified Tooling and Cross-Platform Automation
Operating across disparate public cloud providers requires vendor-neutral tooling that abstracts underlying infrastructure differences. Experienced professionals implement unified delivery pipelines and infrastructure orchestration to ensure seamless management.
- Infrastructure as Code with Terraform, OpenTofu, and Pulumi
- Workload orchestration across cloud boundaries with Kubernetes
- Service connectivity via HashiCorp Consul and Istio service mesh
- Federated identity management, IAM boundaries, and secret stores
Core Drivers for Multi-Cloud Adoption
Organizations adopt multiple cloud platforms to place workloads where specific capabilities shine, such as advanced analytics or enterprise system integrations. Redundancy protects against catastrophic regional outages, while geographic distribution keeps data processing close to end users and compliant with data sovereignty regulations.
Industry Demands Across Munich
Enterprises and scale-ups in Munich, particularly within the automotive, manufacturing, and financial services sectors, face strict regulatory demands. A multi-cloud setup enables these companies to protect IP, meet European data residency requirements, and ensure high availability across international manufacturing hubs.
Managing Complexity and Operational Overhead
Deploying across several clouds introduces distinct IAM models, network topologies, and egress pricing structures. Specialized professionals design landing zones that standardize governance, reduce data transfer overhead, and maintain centralized observability across disjointed monitoring systems.
Hallmarks of Seasoned Cloud Specialists
True specialists understand where cross-cloud portability succeeds and where it introduces needless complexity. They know when to consume native hyperscaler APIs directly and when to wrap capabilities in vendor-agnostic layers, ensuring scalable performance without unsustainable operational drag.
Frequently asked questions
Quick answers to the questions that come up most around Multi-Cloud.
A qualified multi-cloud professional holds deep hands-on expertise in at least two major hyperscalers, typically AWS and Azure or Google Cloud Platform. They demonstrate advanced mastery of vendor-neutral orchestration using tools like Terraform, OpenTofu, and Kubernetes. In addition, they must show a solid track record in designing federated identity models and cross-cloud networking.
Enterprises in Munich often pursue a multi-cloud model to maintain compliance with European Union data sovereignty laws and avoid dependence on a single provider. Automotive, manufacturing, and insurance firms also leverage specific platform capabilities, such as advanced machine learning tools on one platform alongside core ERP systems hosted on another.
A single-cloud setup minimizes operational overhead by using native proprietary services, but it exposes the business to vendor lock-in and provider-level outages. In contrast, multi-cloud architectures improve disaster recovery and negotiation leverage at the cost of managing multiple identity systems, distinct network models, and egress data charges.
Skilled professionals minimize network egress costs by designing loosely coupled systems that avoid continuous large-scale data transfers between clouds. Within a multi-cloud architecture, they route only processed summaries over dedicated interconnects or VPN tunnels and co-locate data-intensive workloads alongside their primary storage engines.
Beyond general cloud administration, multi-cloud strategy requires deep proficiency in container orchestration with Kubernetes, GitOps workflows using ArgoCD or Flux, and centralized secret management with HashiCorp Vault. Security posture management, FinOps cost monitoring, and unified observability tools like Datadog or Prometheus are equally essential.
Yes, cloud infrastructure engineering is inherently remote-friendly because delivery pipelines, code repositories, and control planes operate globally. For organizations based in Munich, specialists often collaborate remotely while attending key planning sessions or compliance kickoffs on-site when strict internal governance demands it.
Most engineering environments and technical infrastructure documentation rely entirely on English. However, companies headquartered in Munich that operate within heavily regulated or traditional industry sectors frequently appreciate multi-cloud professionals who can also navigate project meetings and governance reviews in German.
Review their architectural track record to see how they mitigated vendor lock-in without overengineering abstraction layers. A seasoned multi-cloud specialist should be able to explain the concrete trade-offs they made regarding network latency, security federation, and daily operational burden across platforms.
The average hourly rate of freelancers in Munich, Germany who have used Multi-Cloud in their recent projects is 115 €, which corresponds to a daily rate of about 921 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Multi-Cloud in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Munich, Germany who have used Multi-Cloud in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Munich, Germany who have used Multi-Cloud in their recent projects are English (100%), German (83%), and Romanian (33%).
The most common industries among freelancers in Munich, Germany who have used Multi-Cloud in their recent projects are Information Technology (100%), Automotive (67%), and Telecommunication (67%).
The most common business areas among freelancers in Munich, Germany who have used Multi-Cloud in their recent projects are Information Technology (100%), Operations (83%), and Project Management (83%).
Main locations of FRATCH Experts, who have recently used Multi-Cloud
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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