Argo Workflows Experts in Germany
for reliable data and CI/CD pipelines, matched in minutes with vetted freelancersHire experts who design and operate Kubernetes-native workflows, connect Argo Workflows with CI/CD and data platforms, and deliver reusable pipeline templates. FRATCH matches you quickly and precisely with vetted, available freelancers in Germany.
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.
Gyan P.
Last position:
Senior DevOps & Cloud Architect, Head of Infrastructure at Bosch
- Azure Landing Zone architecture: Design and implement a hub-spoke, multi-region-capable Azure landing zone that provides the secure, scalable and highly available foundation for autonomous driving and parking data/ML platforms.
- Compliance through Azure Policy: Enforce security and compliance standards across Azure subscriptions via Azure Policy (guardrails, deny/audit/deploy-if-not-exists), consistently achieving top-tier infrastructure security scores.
- Infrastructure-as-Code: Automate provisioning and configuration of Azure resources with Terraform and Bicep, with changes delivered through reviewed CI/CD pipelines.
- Azure Databricks and Unity Catalog: Extensive experience operating Azure Databricks with Unity Catalog for centralized data governance, access control, lineage and auditing across workspaces.
- Monitoring and observability: Built and run infrastructure monitoring and logging with Azure Monitor, Prometheus, Grafana and Loki, covering platform health, alerting, troubleshooting and capacity trends.
- Platform at scale: Architected and operate cloud data/ML platforms supporting multi-PB vehicle data ingestion and simulation; orchestrate Argo Workflow-based recompute and autolabeling services scaling to 100k+ concurrent pods on AKS.
- Reproducible ML: Established reproducible training environments with dataset versioning and provenance tracking, enabling auditing and experiment reliability.
- Operational excellence: Automated GitHub organization management and CI/CD pipelines, improving developer productivity and deployment reliability; automated recurring operational processes.
- FinOps: Developed transparent resource utilization and cost-tracking for multi-project Azure environments.
- Leadership: Technical lead for infrastructure topics, collaborating with development, DevOps and security teams; technical interviewer and recruitment panel member for scaling the engineering team.
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
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.
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
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
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
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.
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
19 years
Position duration
1.7 years
Positions per freelancer
13
Top business areas
Information Technology, Product Development, Project Management
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
50%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100%
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 Germany 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 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 29 Oct 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 (75%)
- Automotive (50%)
- Retail (50%)
- Insurance (42%)
- Manufacturing (42%)
- Media and Entertainment (42%)
- Transportation (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Kubernetes-native orchestration
Argo Workflows is an open-source workflow engine for Kubernetes. It runs multi-step jobs as containerized workflows defined in YAML, with dependencies, parameters, artifacts and conditional logic. Teams use it to coordinate data processing, machine learning pipelines, batch operations and CI/CD tasks inside Kubernetes clusters.
Pipeline building blocks
A workflow can contain sequential steps, parallel branches, retries, timeouts and approval points. Templates make recurring tasks reusable across teams, while DAGs express complex dependencies without forcing every task into a single sequence. Inputs, outputs and artifacts connect containers into a traceable execution flow.
Ecosystem and tooling
Argo Workflows is part of the Argo ecosystem and commonly works alongside Argo CD, Argo Events and Argo Rollouts. Strong specialists also work with Kubernetes, Helm, container registries, object storage and observability tools. They typically manage manifests in Git and integrate secrets, identity, logging and monitoring into production workflows.
Where companies use it
- Data preparation and scheduled batch processing
- Machine learning training, evaluation and model pipelines
- Container builds, tests and release automation
- Event-driven jobs triggered by repositories, queues or APIs
- Reproducible scientific and analytical workloads
When to bring in expertise
Companies often seek freelance expertise when workflows have outgrown scripts, cron jobs or a basic CI runner. A specialist can establish cluster-safe patterns, migrate existing pipelines, reduce fragile dependencies and define reusable templates. In Germany, remote collaboration is common, while regulated or operational environments may require on-site workshops and clear German or English documentation.
What strong specialists deliver
A capable professional understands both workflow logic and Kubernetes operations. They design idempotent tasks, resource requests, artifact retention, failure handling and access controls rather than only writing YAML. They also test recovery paths, document ownership and connect workflow health to meaningful alerts, giving teams a dependable foundation for ongoing changes.
Frequently asked questions
What clients ask us most about Argo Workflows — answered in short.
Argo Workflows runs containerized, multi-step processes on Kubernetes. Companies use it for data pipelines, machine learning workloads, batch processing, CI/CD automation and other jobs that need dependencies, parallel execution or repeatable runs.
Argo Workflows is designed around Kubernetes-native execution, while GitHub Actions and Jenkins are commonly centered on repository automation and general CI/CD. Argo Workflows is a strong choice when teams need DAG-based orchestration, Kubernetes scheduling and workflow artifacts; the best option depends on existing infrastructure and operating practices.
Argo Workflows orchestrates tasks and pipelines that run as Kubernetes workloads. Argo CD focuses on continuously synchronizing application manifests from Git to a cluster. They can work together, with Argo CD deploying workflow definitions and Argo Workflows executing operational or data processes.
A strong Argo Workflows specialist usually understands Kubernetes, Docker or OCI images, YAML, Git and Helm. Useful adjacent skills include Argo CD, Argo Events, cloud storage, secrets management, observability, CI systems and data or machine learning tooling.
The right level depends on the scope. A simple migration may need someone who can model templates and dependencies, while a production platform requires proven Kubernetes operations, security design, resource management and recovery testing. Ask for examples of workflows that handle failure, retries and operational ownership.
Yes, many Argo Workflows projects are suitable for remote collaboration because definitions, reviews and deployments can be managed through Git and secure cluster access. On-site work may still help with workshops, regulated environments or coordination with infrastructure teams. German or English communication should match the project context.
Look for workflow designs that are reusable, observable and safe to retry. A capable Argo Workflows professional should explain artifact handling, resource limits, secrets, access controls, versioning and failure recovery, not just provide a working YAML file.
Argo Workflows can replace many scripts, cron-based processes and pipeline jobs when containerized, traceable execution is valuable. It may not be the right fit for every lightweight task or non-Kubernetes environment. A specialist should first assess runtime needs, dependencies, scheduling and migration effort.
The average hourly rate of freelancers in Germany who have used Argo Workflows in their recent projects is 106 €, which corresponds to a daily rate of about 847 € 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 50% hold at least a Master's degree.
On average, freelancers in Germany who have used Argo Workflows in their recent projects have 19 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 (25%).
The most common industries among freelancers in Germany who have used Argo Workflows in their recent projects are Information Technology (92%), Banking and Finance (75%), and Automotive (50%).
The most common business areas among freelancers in Germany who have used Argo Workflows in their recent projects are Information Technology (100%), Product Development (75%), and Project Management (75%).
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.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
