Developed a highly available Kubernetes cluster with GitOps to optimize application delivery and development processes
Set up the Amazon EKS cluster using AWS CDK, including automated tests with Jest to ensure correct configuration.
Established a GitOps workflow with Flux to manage automated, versioned deployments from Git. Changes to the repository are immediately rolled out to Kubernetes, eliminating manual steps and enabling faster, reproducible deployments.
Implemented OIDC and RBAC for secure authentication and authorization.
Used Helm and Kustomize for dynamic, versioned Kubernetes deployments.
Developed and integrated CI/CD pipelines with GitLab that interact seamlessly with the GitOps process and automatically roll out deployments from the repository.
Fully automated deployments without manual intervention by DevOps engineers -> drastic reduction in deployment times.
Improved consistency and traceability of infrastructure changes by combining GitOps and CDK.
Increased security and compliance through end-to-end authentication (OIDC & RBAC) and versioned infrastructure deployments.
More efficient development processes as teams push their changes directly via Git and Flux ensures secure, automated distribution.
Technologies used: AWS IAM, AWS EKS, AWS EC2, AWS VPC, AWS S3, OIDC, RBAC, AWS CloudWatch, AWS CloudFormation, AWS Fargate, GitOps (Flux), GitLab, TypeScript, AWS CDK, Helm, Kustomize, Jest.
A client needed an SFTP solution for secure data exchange between mobile devices. AWS Transfer for SFTP did not offer an out-of-the-box solution for user authentication.
Developed a custom authorizer based on AWS API Gateway and Lambda.
Integrated the solution into existing AWS workflows.
Created a secure and scalable authentication framework.
Secure authentication of devices via individual user logins.
Reduced manual administration effort by automating processes.
Technologies used: AWS IAM, AWS CloudTrail, AWS CloudWatch, AWS Fargate, GitLab, TypeScript, AWS CDK, AWS Lambda, AWS API Gateway.
The client's service team wanted to set up an automated cloud infrastructure with clearly defined processes for Infrastructure-as-Code (IaC) and introduce maintainable CI/CD pipelines. They also required a standardized AMI image creation for Windows and Linux using EC2 Image Builder.
Automated the GitLab Runner infrastructure (setup, updates, and operations).
Introduced AWS CDK for creating and managing IaC templates.
Developed CI/CD pipelines tailored flexibly to client requirements.
Integrated EC2 Image Builder to automate Windows and Linux AMI image provisioning - published as InnerSource under Free4All in GitLab.
Conducted workshops & enablement for the service team.
The service team now has a full CI/CD infrastructure and can independently deploy IaC templates as well as preconfigured Windows and Linux images.
The introduction of AWS CDK and EC2 Image Builder led to simplified management and scaling of the infrastructure.
The deployment time for new server instances was significantly reduced by using preconfigured AMIs.
Technologies used: AWS IAM, AWS EC2, AWS SAM, AWS Step Functions, AWS VPC, AWS S3, AWS CloudTrail, AWS CloudWatch, AWS AutoScaling, AWS SSM, EC2 Image Builder, Windows Server 2022, PowerShell, AWS Fargate, GitLab, TypeScript, AWS CDK, Citrix.
A client in the energy sector needed a secure solution to allow development teams to deploy on-premises automatically while ensuring communication with Azure DevOps.
Built a secure architecture capable of provisioning Azure DevOps agent pools.
Configured on-premises firewalls to ensure secure traffic between internal services (Nexus, SonarQube) and the cloud.
Provided monitoring features to ensure data integrity.
Automated provisioning of Azure DevOps agents within the on-premises environment.
Improved development cycles by integrating CI/CD processes with on-premises resources.
Technologies used: AWS IAM, AWS EKS, AWS EC2, AWS SAM, AWS Step Functions, AWS VPC, AWS S3, AWS CloudTrail, AWS CloudWatch, AWS CloudFormation, AWS Fargate, Azure DevOps, Terraform.
The client needed an automated deployment solution for AWS resources and containers that supports multi-account patterns and makes existing processes more efficient.
Developed a deployment pipeline based on GitLab CI/CD using IAM role credentials for secure access.
Implemented an AWS Landing Zone for structured management of multi-account environments and to ensure governance and security policies.
Used Docker containers for GitLab runners to optimize resource management.
Implemented AWS CloudFormation to provision infrastructure components.
Introduced a stable, reusable deployment pipeline that supports multi-account scenarios.
With the AWS Landing Zone, created a unified, scalable account structure that covers centralized security policies, network configurations, and compliance requirements.
Reduced manual configuration efforts and improved deployment speed.
Technologies used: AWS IAM, AWS ECS, AWS, AWS VPC, AWS S3, AWS CloudTrail, AWS CloudWatch, AWS CloudFormation, AWS Fargate, AWS Config, GitLab, GitLab CI/CD, Docker, Landing Zone.
A client wanted to migrate their entire IT infrastructure to the AWS public cloud to benefit from greater scalability, cost efficiency, and flexibility. The migration covered large systems and needed to happen without disrupting ongoing operations.
Collaborated in an internal, agile team at the client (Scrum and Kanban).
Built CI/CD pipelines to automate resource provisioning.
Advised on best practices for migration, including setting up monitoring and security solutions.
Collaborated in an agile team at the client (Scrum and Kanban).
Trained internal developer teams on using the new cloud environment.
Introduced CI/CD pipelines that reduced resource provisioning time.
Improved scalability: The infrastructure is now flexible to growing demands.
Technologies used: AWS IAM, AWS EC2, AWS OpsWorks (Chef), AWS VPC, AWS VPN, AWS S3, AWS S3 Glacier, AWS Route 53, AWS Step Functions, AWS CloudTrail, AWS CloudWatch, AWS CloudFormation, AWS API Gateway, AWS Lambda, AWS SNS, AWS SQS, AWS Auto Scaling, AWS SSM, Windows Server, AWS RDS, AWS KMS, AWS ECS, AWS Config, AWS DynamoDB, AWS CloudFront.
The client needed a workflow solution to flexibly control data processes, pause workflows, and ensure raw data was processed on time.
Built workflow orchestration using AWS Step Functions for flexible control and monitoring.
Ensured data integrity through ETL processes with clearly defined milestones and timelines.
Developed functions for monitoring and error handling.
Data was successfully fetched and passed on, always within the defined time goals.
Automation minimized manual interventions and increased process reliability.
Technologies used: AWS IAM, AWS EC2, AWS OpsWorks (Chef), AWS VPC, AWS S3, AWS Route 53, AWS Step Functions, AWS CloudTrail, AWS CloudWatch, AWS CloudFormation, AWS API Gateway, AWS Lambda, AWS SNS, Windows Server, AWS SSM, PowerShell.
Experienced Cloud and DevOps consultant with over 12 years of expertise, including cloud architectures, Kubernetes, and CI/CD pipelines. Specialized in GitOps (Flux / ArgoCD) and Infrastructure-as-Code (AWS CDK, Terraform). Develops customer-focused solutions for more efficient software delivery and optimized development processes.
Technical expertise: Expert in GitOps (Flux / ArgoCD) and Infrastructure-as-Code (AWS CDK, Terraform) for automating Kubernetes and cloud environments.
Consulting skills: Successful implementation of cloud and DevOps solutions for international clients.
Communication: Confident in interacting with technical and non-technical stakeholders.
Flexibility: Adaptable to new technologies and challenges.
Tailored solutions: Custom cloud and IT strategies that reduce costs and optimize processes.
High reliability: Independent, structured, and punctual way of working.
Team player mentality: Promotes collaboration in agile teams.
Future orientation: Ongoing learning to always apply the latest technologies.
Practical consulting: Hands-on approach with measurable added value for your projects.
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