Google Cloud Run Experts in Germany
matched in minutes from over 15,000 CVsHire experts who deploy containerized applications, design event-driven services and connect workloads with Google Cloud databases and APIs. FRATCH finds vetted, available freelancers whose skills match your Google Cloud Run project precisely and quickly.
Meet FRATCH Experts in Germany, who have recently used Google Cloud Run
Qamar H.
Last position:
Freelance Consultant Data Analytics & AI Portfolio at TIC Company
- Support for a data, analytics and AI initiative in a regulated enterprise environment by structuring, evaluating and prioritizing several data-driven use cases based on business impact, feasibility, scalability, data maturity and governance requirements.
- Translation of complex business and analytics requirements into clear product, data and implementation logic, as well as preparation of decision-ready documents, target visions and roadmap inputs for stakeholder and management discussions.
Khalid E.
Last position:
Lead Architect & Developer at kem-consulting
Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.
Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.
Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.
Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.
Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.
Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.
Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self hosted solutions
- Development of reusable agentic workflows and mini applications that enable non technical employees to solve business problems independently
- Implementation of internal business applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
- Implementation of nine mini applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
- Techstack: Python, Nextjs, Typescript, Streamlit, Anthropic SDK (Claude), Azure, Linux Ubuntu, PostgreSQL, MS SQL, Angular, Authentik
Boian V.
Last position:
Solution Architect at DB InfraGO AG
The Base Services of DB InfraGO form a central data hub between the company’s IT systems. Common Data Services are developed that distribute data from source systems to a variety of downstream systems (via JMS) and make them available (via REST API). The existing service landscape based on TIBCO is being migrated to DB InfraGO’s cloud-native platform.
- Architecture design and implementation for performance-optimized bulk data processing of several million datasets at specific times during the day
- Technical specification and documentation of business requirements
- Integration of various subsystems (including SAP and Salesforce) through the reimplementation of more than 40 microservices based on Spring Boot
- Migration of TIBCO Based microservices from the Enterprise Integration Platform to the Cloud Native Platform using Spring-Boot
- Establishment of a deployment pipeline using GitLab CI/CD, Artifactory, and automated deployment to a Kubernetes environment with the help of ArgoCD
- Definition and implementation of automated unit, integration, and regression tests
Team Size: 9
Technologies/Tools: Java, Spring Boot, ActiveMQ(JMS), JUnit, Tibco Business Works, Gitlab (CI/CD), Kubernetes (Amazon AWS), ArgoCD, postgreSQL, Oracle, Postman, Hoppscotch, openAPI, Sonarqube
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Daniel A.
Last position:
Sales Development Representative (SDR) at TenderFlow GmbH
- Acquires new B2B customers for an AI SaaS startup in the public tendering space and books product demos with IT decision-makers.
- Qualifies target customers based on a defined ideal customer profile, including discovery, needs analysis, and objection handling.
- Builds domain knowledge in public procurement (EVB-IT, German and EU procurement portals) for conversations on equal footing.
Laurin H.
Last position:
Software Architect (Freelance) at Care4Sure
- Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
- Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.
Deepak M.
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
Torsten F.
Last position:
Data Analyst, Requirements Manager at Isabellenhütte Heusler GmbH
Analysis of the existing reporting platform including processes and governance topics with stakeholders from sales and marketing.
Detailed analysis and evaluation of client-defined requirements for existing reporting and new dashboards.
Supporting stakeholders in managing sales processes and early detection of KPI trends.
Use of Microsoft Power BI as central analysis and reporting platform.
Developing a proposal for the necessary evolution of processes and the Power BI platform.
Gathering current business processes and defining company-wide KPIs in coordination with stakeholders.
Analysis and inventory of the client's Power BI platform.
Analysis of processes and data governance.
Recording and documenting current business processes.
Developing recommendations for process and reporting platform improvements.
Designing and implementing dashboards in Power BI.
Defining company-wide KPIs and aligning them with stakeholders.
Microsoft Power BI.
Data analytics.
KPI definition and reporting.
Dashboard design and data visualization.
Stakeholder management and requirements management.
Haseeb Z.
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Daniel S.
Last position:
Senior Software Engineer at energielenker solutions GmbH
- Designed and implemented a Python-based ETL pipeline with the Dagster framework to transform raw energy data from heterogeneous sources using InfluxDB and visualizations in Grafana
- Defined time-based and dependency-based jobs
- Deployed to managed Kubernetes clusters using Helm
- Integrated InfluxDB Cloud
- Prepared data for use in Grafana, including cleaning, normalization, and time-based resampling in Python
- Developed dashboards and visualizations in Grafana
- Developed unit tests with mocking using pytest
- Set up a CI/CD pipeline in GitLab
Technologies: Python, Dagster, InfluxDB, Grafana, pandas, pytest, REST, CI/CD, GitLab, Container, Kubernetes, Helm, Docker, Cloud
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- 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
Patrick E.
Last position:
Honorary Lecturer at SRH University Berlin
- Cloud Computing Fundamentals & Architecture: Expertise in core cloud concepts, including the three main Service Models (IaaS, PaaS, SaaS) and diverse Deployment Models (Public, Private, Hybrid, Multi-cloud).
- Modern Application Deployment Strategies (GCP Focus): Instruction on the GCP Application Hosting Spectrum, covering Virtual Machines, Containers (Kubernetes and Cloud Run), Platform as a Service (App Engine), and Serverless Computing (Functions as a Service - FaaS).
- Data Management & Big Data Analytics: Comprehensive coverage of Cloud Storage options (Object, Block, File) and Database solutions, including Relational (Cloud SQL), NoSQL (Firestore, BigTable, Memorystore), and serverless enterprise data warehousing (BigQuery).
- DevOps and Infrastructure Automation: Skills in DevOps principles, including Continuous Integration (CI), Continuous Delivery (CD), Infrastructure as Code (IaC) using tools like Terraform, and implementing effective Monitoring and Logging for system observability.
- Emerging Technologies & Responsible Cloud Use: Focus on crucial topics like Cloud and IoT Security, Identity and Access Management (IAM), data privacy, and the ethical considerations of cloud and massive data collection.
Vitalijs V.
Last position:
Mentor at EdSyl
- Mentored participants through structured learning paths and hands-on projects.
- Provided feedback and guidance on AI integration and product management best practices.
- Mentor teams on AI adoption, enterprise product strategy, and practical delivery choices.
Moritz K.
Last position:
Senior DevOps Engineer GCP at tedi GmbH & Co. KG
- Design and implementation of DevOps and CI/CD practices for data and analytics teams
- Introduction of infrastructure as code with Terraform (IaC)
- Setup and maintenance of GCP user and permission management with Terraform in multi-project environment
- Design and implementation of CI/CD pipelines with GitHub
- Leading and training developer team for the introduction of IaC and CI/CD practices
- Building and optimising database connectors with Apache Arrow for terabyte scale data extraction (Oracle, SAP)
- Optimising data lake storage and warehouse ingest
Discover over 15,000 top freelancers
Statistics of experts using Google Cloud Run
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.8 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
96%
Master's degree or higher
56%
Doctorate
4%
Certifications per freelancer
4
Most common languages
English, German, Spanish
Speak two or more languages
97%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Google Cloud Run
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Google Cloud Run 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 (97%)
- Banking and Finance (55%)
- Education (45%)
- Retail (39%)
- Government and Administration (35%)
- Media and Entertainment (32%)
- Professional Services (32%)
- Automotive (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Cloud Run is
Google Cloud Run is a fully managed serverless platform for running containerized applications on Google Cloud. It starts services on demand, handles incoming traffic and scales instances without requiring a company to manage servers or clusters. Teams use it for APIs, web applications, background workers and internal services.
Where it fits
Cloud Run works well when a workload can run as a stateless container and needs flexible scaling. It supports standard HTTP services, webhooks, scheduled jobs through Cloud Scheduler and asynchronous processing with Pub/Sub. Companies in Germany use it across software, commerce, media and industrial systems where rapid delivery and controlled operations matter.
Ecosystem and tooling
A strong Cloud Run setup usually connects application code with Google Cloud services and a repeatable delivery process.
- Build images with Docker and store them in Artifact Registry
- Automate releases with Cloud Build, Cloud Deploy or GitHub Actions
- Connect services to Cloud SQL, Firestore, Memorystore and Pub/Sub
- Manage configuration with Secret Manager and environment variables
- Observe requests, logs and traces with Cloud Monitoring and Cloud Logging
Typical project work
Freelance specialists support both new services and existing Google Cloud estates. They create container build processes, configure revisions and traffic splitting, secure service access, and connect Cloud Run to identity, data and messaging components. They may also move workloads from virtual machines, App Engine or Kubernetes when a simpler operating model is appropriate.
When expertise matters
Companies often bring in outside expertise when a service must reach production quickly or when a first Cloud Run deployment has exposed operational gaps.
- Establish a reliable container and release workflow
- Reduce cold-start impact and improve request handling
- Configure private networking, IAM and service-to-service access
- Set up observability, alerts and rollback procedures
- Review architecture, resource settings and cloud spend controls
What strong specialists bring
Strong professionals understand both containers and Google Cloud operations. They know where Cloud Run differs from GKE, App Engine and Compute Engine, and can explain those trade-offs in practical terms. Look for evidence of secure deployments, clear infrastructure documentation, tested failure handling and experience collaborating remotely or on site with teams in Germany.
Frequently asked questions
Everything clients usually want to know about Google Cloud Run, in one place.
Google Cloud Run is used to run containerized HTTP services, APIs, websites, webhooks and event-driven workers without managing the underlying servers. It is also suitable for scheduled tasks and small services that need to scale with demand. A specialist can assess whether the workload is stateless enough for Cloud Run and select the right supporting Google Cloud services.
Google Cloud Run offers less infrastructure management than GKE or Compute Engine and more container control than a purely code-focused App Engine deployment. GKE is usually stronger for complex orchestration, persistent workloads and detailed cluster control, while Compute Engine suits workloads that need direct virtual-machine access. The right choice depends on runtime requirements, networking, scaling and the operating skills available in the team.
A capable Google Cloud Run specialist often works with Docker, IAM, Artifact Registry, Cloud Build, Terraform and Git-based delivery. Useful adjacent knowledge includes Cloud SQL, Pub/Sub, Secret Manager, Cloud Monitoring, networking and application security. Experience with the application language and its framework is also important because platform settings cannot compensate for inefficient service code.
The required background depends on the project’s risk and scope, not on the product name alone. A simple container deployment may need focused delivery experience, while a regulated or multi-service environment calls for a Cloud Run professional who has handled identity, private networking, observability, failure recovery and release controls. Ask for examples that resemble the intended architecture.
Google Cloud Run work is well suited to remote collaboration because configuration, deployments and monitoring are handled through shared cloud tooling. Teams in Germany should still agree on working hours, documentation standards, access procedures and communication language. On-site workshops can help with architecture decisions, but ongoing delivery is often effective remotely.
Ask a Google Cloud Run professional to explain a deployment from source commit to production, including image security, IAM, secrets, rollbacks and monitoring. Good specialists make trade-offs clear and can describe how they handled timeouts, concurrency, cold starts and service dependencies. A short architecture review or practical assessment can reveal more than a list of tools.
Cloud Run supports event-driven designs through services such as Pub/Sub, Eventarc and Cloud Scheduler. A specialist can build workers that process messages, handle retries and keep operations observable without coupling every component to a permanent server. The design still needs careful attention to idempotency, time limits and duplicate delivery.
Before starting a Google Cloud Run engagement, clarify the container contract, deployment path, expected traffic patterns, access boundaries and ownership of the Google Cloud project. Confirm whether infrastructure is managed with Terraform or another defined approach, and agree on documentation and handover expectations. These details determine whether the work is a focused service delivery or a broader platform assignment.
The average hourly rate of freelancers in Germany who have used Google Cloud Run in their recent projects is 94 €, which corresponds to a daily rate of about 754 € based on an 8-hour working day.
Of the freelancers in Germany who have used Google Cloud Run in their recent projects, 96% hold at least a Bachelor's degree, 56% hold at least a Master's degree, and 4% hold a doctorate.
On average, freelancers in Germany who have used Google Cloud Run in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Germany who have used Google Cloud Run in their recent projects are English (100%), German (90%), and Spanish (16%).
The most common industries among freelancers in Germany who have used Google Cloud Run in their recent projects are Information Technology (97%), Banking and Finance (55%), and Education (45%).
The most common business areas among freelancers in Germany who have used Google Cloud Run in their recent projects are Information Technology (100%), Product Development (94%), and Business Intelligence (61%).
Main locations of FRATCH Experts, who have recently used Google Cloud Run
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
