Argo Workflows Experts in Germany
to automate data and container workflows with vetted, available freelancersHire experts who design Kubernetes-native pipelines, connect containerized jobs and manage reliable DAG execution with Argo Workflows, Argo Events and artifact repositories. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Argo Workflows
Ales L.
Last position:
Senior DevOps Consultant (Freelance) at European Union Agency (via IBM)
- Worked as freelance Senior DevOps Consultant on-site for IBM at a European Union Agency, operating in a highly secure, air-gapped environment managing classified systems.
- Led automation and DevOps initiatives for a large-scale OpenShift platform (>400 nodes), driving deployment efficiency, GitOps adoption, and operational automation using Ansible, Python, and Bash while ensuring compliance with security requirements.
- Spearheaded automation of release and deployment workflows in a private cloud environment hosting 400+ OpenShift nodes, significantly improving deployment speed and reliability.
- Migrated existing playbooks, roles, and templates from Ansible Tower to Ansible Automation Platform (AAP), ensuring full compliance with fully-qualified collection names (FQCN) and preparing custom Execution Environments (EE) for containerized automation.
- Implemented GitOps Agent for AAP Controller Configuration as Code, enabling automated synchronization (CRUD) of Ansible Controller objects based on repository-stored configuration definitions using GitHub webhooks.
- Designed and automated complex multi-step operational workflows including environment cleanup, Helix cluster component re-creation, Kafka topic management, and OpenShift object lifecycle management across ~100 environments.
- Achieved a reduction of multi-day manual operations to under a few hours through automation improvements spanning multiple AAP clusters and OpenShift environments.
- Integrated Ansible Automation Platform with Thycotic (Delinea) Secret Server via lookup plugin to enhance secure credential management in automated processes.
- Managed deployment tasks, platform troubleshooting, and Istio network configurations while adhering to stringent EU PSC security and compliance standards.
- Collaborated with infrastructure and application teams to refine deployment procedures, develop naming conventions, and continuously improve automation coverage in an air-gapped, classified environment.
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
Olaf R.
Last position:
DevOps Architect / Consultant at Authority with increased security requirements
- Identification of requirements (legal, organizational, and technical)
- Design of solution architectures
- Evaluation of concepts and technologies
- Preparation of decision templates
- Architectural Decision Records (ADR)
- Coordination of implementation
- Review of implementations
- Documentation
Marc M.
Last position:
Freelance Data Specialist at BrightlySoftware – A Siemens Company
- Migration of customer data from a private cloud to AWS
- Optimizing data transformation jobs and migration from Talend to AWS Glue
- Automation of all migration steps
- Used technologies: AWS, Python, Lambda, CloudFormation, SQLServer, AWS Stepfunctions, Glue, PySpark
Vitaliy R.
Last position:
DevOps GitOps (temp) at Signal Iduna
- 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.
Sebastian S.
Last position:
Group Product Manager – Digital Platform Discovery at SPREAD.AI
- Developed and implemented organization-wide discovery framework based on Ulwick’s Outcome-Driven Innovation; enabled 7 Product Owners to systematically identify and quantify unrealized value through shared outcome language and opportunity scoring methodology
- Transformed Product Owner role from backlog clerks to strategic experimenters; established dedicated time budget for autonomous hypothesis testing and discovery activities
- Rebuilt customer journey maps to start at actual user need (tool selection phase) instead of platform entry point; eliminated manual data aggregation work previously done by project teams
- Implemented OKR framework across 4 product teams; defined quarterly objectives with measurable key results (e.g., 40% reduction in manual integration effort, self-service adoption increase)
- Unified 3 separate platform roadmaps through cross-team dependency mapping and shared service agreements
- Supported enterprise sales cycle with ROI modeling and technical due diligence for automotive and defense customers
Marco L.
Last position:
Senior IT Consultant | Cloud Data Engineer | Infrastructure Architect at Hannover Rück SE
Built an enterprise data lakehouse platform on Azure Databricks
Developed production data pipelines and governance structures
Implemented private cloud infrastructures using Terraform
Introduced modern CI/CD standards in Azure DevOps
Implemented secure IAM and governance concepts
Developed scalable PySpark and Delta Lake frameworks
Supported self-service analytics and data product approaches
Provided architecture and platform consulting for enterprise data initiatives
Built a central DataHub architecture for insurance data
Integrated multiple subsystems into a lakehouse platform
Introduced data governance and data lineage
Supported modern analytics and reporting standards
Optimized data delivery for business and analytics teams
Oluwasegun A.
Last position:
Observability Specialist at ING GmbH
- Requirements analysis for the enterprise-wide observability platform.
- Creation of playbooks and pipelines for rolling out Envoy, OpenTelemetry Collectors, and OpenTelemetry Agents.
- Conducting load tests for capacity planning of metrics for the observability platform.
- Documentation and implementation of compliance standards for production readiness.
- Creation and design of RED metrics, spanmetrics, JBoss, and Tomcat dashboards for mission-critical applications.
- Setting up alerts for critical applications for proactive incident response.
- Integration of OpenShift applications into the enterprise-wide observability stack.
Torsten G.
Last position:
Data Vault with Informatica on Oracle at SEFE
- Data Vault modeling and performance improvements in Oracle.
- Requirements process with model-driven Data Vault automation.
- Preparation of business requirements for the price database in the gas and electricity market.
- Sales performance based on Salesforce CRM.
- Tasks: Concept and development.
- Tools used: Informatica, Oracle, DBT, Docker, DataOps, Airflow Workflow, Python Ingestion, Gitlab CI.
Gyan P.
Last position:
Senior DevOps and Cloud Architect at Bosch
- Architected and operated cloud-based data and ML platforms for autonomous driving and parking systems, supporting large-scale (multi PB scale) simulation and vehicle data ingestion.
- Implemented security, compliance, and governance standards across Azure subscriptions and cloud resources.
- Managed GitHub organizations and CI/CD pipelines to improve deployment reliability and developer productivity.
- Contributed to hiring and technical interviews as part of the recruitment panel.
Jonathan K.
Last position:
Test Automation Engineer (Freelancer) at Bank Verlag GmbH
- Refactoring test automation (Java/Kotlin)
- Implementing integration tests (Cucumber, Gherkin)
- Bug fixing and implementing new features in pair programming
- Implementing test processes in CI/CD architecture (GitLab)
- Planning and creating a test strategy
- Analyzing business processes to assess test coverage
- Bug tracking and verification (Kubernetes/k8s, Kibana)
- Extending the deployment pipelines in GitLab CI
- Evaluating logs and metrics
- Implementing tests to verify security requirements (PCI DSS/ISO 27001)
Andreas S.
Last position:
Lead Developer at Software
- Extended the document management system with a standard CMIS (Content Management Interoperability Services) interface
- Implemented CMIS core services like navigation, access rights, search, CRUD operations, and versioning in Java
- Implemented based on RESTful / OpenAPI services
- Delivered as a fat-jar and native container image
- Deployed on-premises and serverlessly as an Azure Container Application using Terraform
- Improved team autonomy through infrastructure engineering and short feedback loops
- Established observability with OpenTelemetry, Azure Monitor, and Azure Logic Apps
- Introduced Terraform and trunk-based development processes
- Ensured quality with BDD tests in C# using SpecFlow and Testcontainers
- Created Azure DevOps pipeline integration tests
- Introduced cloud deployment processes
- Trained staff in cloud and Terraform
Pit W.
Last position:
Devops & Site Reliability Engineer at Alpin Analytics GmbH
- Planning and building a multi-tenant data analytics platform based on bare metal Kubernetes
- Designing a cloud native data ingest architecture incorporating Argo Workflows and Argo Events
- Technologies and tools: Kubernetes, Docker, Helm, Argo Workflows, Argo Events
Discover over 15,000 top freelancers
Statistics of experts using Argo Workflows
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
1.7 years
Positions per freelancer
13
Top business areas
Information Technology, Project Management, Operations
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Operations, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
63%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100%
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 Argo Workflows
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.
Argo Workflows 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 (92%)
- Banking and Finance (77%)
- Automotive (54%)
- Insurance (38%)
- Media and Entertainment (38%)
- Professional Services (38%)
- Government and Administration (38%)
- Retail (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Argo Workflows does
Argo Workflows is an open-source container-native workflow engine for Kubernetes. It runs multi-step jobs as YAML-defined workflows, with each step packaged in a container. Teams use it for data processing, machine learning pipelines, CI tasks, batch automation and repeatable operational processes.
Core workflow patterns
Workflows can run steps in sequence, in parallel or as directed acyclic graphs. Parameters, artifacts, retries, timeouts and conditional execution help teams model complex processes without hard-coding orchestration logic.
- Design reusable templates and workflow specifications
- Coordinate containerized tasks across Kubernetes namespaces
- Pass artifacts between steps and external storage
- Add approvals, schedules, retries and failure handling
Ecosystem and tooling
Argo Workflows operates within the Kubernetes ecosystem and commonly connects with Helm, kubectl, container registries and object storage such as S3-compatible systems. Related Argo projects include Argo Events for event-driven triggers and Argo CD for GitOps delivery, while Prometheus and Grafana support observability.
When companies need specialists
Companies bring in freelance expertise when pipelines have become difficult to maintain, workloads need dependable parallel execution or teams are moving batch processing onto Kubernetes. Specialists can also support platform migrations, reusable workflow libraries and production hardening for data-intensive operations in German and international environments.
- Replace scripts and cron chains with observable workflows
- Connect event sources, APIs, queues and storage systems
- Improve resource management, security and execution reliability
- Document workflows for internal teams and operations
Skills that matter
Strong professionals combine Argo Workflows with Kubernetes, Docker, YAML and Linux fundamentals. They understand workflow templating, RBAC, service accounts, secrets, artifact repositories and resource requests, and can integrate Python or shell tasks with databases, cloud services and data platforms.
What good delivery looks like
Quality work produces workflows that are readable, testable and safe to rerun. Experienced specialists define clear inputs and outputs, isolate permissions, preserve useful logs and design recovery paths for partial failures. They also explain operational ownership, cost controls and local collaboration needs clearly, whether the engagement is remote or includes on-site work in Germany.
Frequently asked questions
What clients ask us most about Argo Workflows — answered in short.
Argo Workflows is used to orchestrate containerized tasks on Kubernetes. Typical applications include data pipelines, machine learning processing, batch jobs, CI automation and scheduled operational workflows.
Argo Workflows is designed around Kubernetes and container execution, while Airflow focuses strongly on scheduling and monitoring data workflows and Jenkins is widely used for CI automation. The right choice depends on the existing platform, workload model, team skills and need for Kubernetes-native execution.
A strong Argo Workflows specialist usually understands Kubernetes, Docker, Linux, YAML, RBAC and object storage. Experience with Helm, Argo Events, Argo CD, Prometheus, Grafana, Python or shell scripting can also be valuable.
The required depth depends on the workflow complexity and production risk. A straightforward internal pipeline may need focused workflow expertise, while multi-tenant Kubernetes environments, regulated data or complex recovery requirements call for a professional who has operated Argo Workflows in production.
Yes. Argo Workflows is configured through version-controlled manifests and can be reviewed, tested and operated remotely. Teams in Germany should clarify working hours, documentation standards, language expectations and whether occasional on-site collaboration is needed.
Argo Workflows supports retries, timeouts, dependencies, conditional steps and failure handling at workflow and template level. A specialist should also plan idempotent tasks, useful logs, artifact retention and safe recovery from partially completed runs.
Look for clear examples of containerized pipelines, reusable templates and Kubernetes integration rather than workflow files alone. Ask how the professional handled permissions, secrets, observability, resource limits and failures, especially in systems operated by teams in Germany.
Argo Workflows can execute workflows in response to external events, often together with Argo Events. This combination can connect webhooks, message systems, schedules or cloud events to controlled Kubernetes-native processing.
The average hourly rate of freelancers in Germany who have used Argo Workflows in their recent projects is 105 €, which corresponds to a daily rate of about 841 € based on an 8-hour working day.
Of the freelancers in Germany who have used Argo Workflows in their recent projects, 100% hold at least a Bachelor's degree and 63% hold at least a Master's degree.
On average, freelancers in Germany who have used Argo Workflows in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Germany who have used Argo Workflows in their recent projects are German (100%), English (100%), and French (23%).
The most common industries among freelancers in Germany who have used Argo Workflows in their recent projects are Information Technology (92%), Banking and Finance (77%), and Automotive (54%).
The most common business areas among freelancers in Germany who have used Argo Workflows in their recent projects are Information Technology (100%), Project Management (77%), and Operations (69%).
Main locations of FRATCH Experts, who have recently used Argo Workflows
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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
