Observability Experts in Hamburg

• 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

• Remodeling of the system architecture for an Azure-based platform aimed at rapid development of new functionalities
• Modeling of a staging concept for the fulfillment of diverse customer and QA needs
• Creation of requirements for, and oversight of, a proof-of-concept supplier project for a Flutter app with highly advanced BLE functionalities
• Comparison of multiple observability platforms for feasibility and requirements fit within the project environment
• Creation of a mobile app architecture based on domain-driven architecture
• Position of technical advisor and accountable solution architect for two development teams
• Execution of architecture reviews and alignment of changes with architectural expectations
• Skills & Technologies: Microsoft Azure , App Services, NodeJS Mono-repository, microservice architecture, domain driven design, self contained systems, requirements engineering, CI/CD, DevOps, API design, solution architecture

traigo is VTG's digital rail logistics and fleet management platform. It processes large volumes of telemetry, mileage, geofence, sensor and wagon-movement events in near real time and provides operational services for rail logistics customers across Europe.

As part of Team Customer Selfcare, I worked on the design, implementation, optimisation and operation of large-scale backend services and event-driven processing pipelines — covering both feature development and operational ownership of business-critical production systems. I also regularly acted as first responder for production incidents, data inconsistencies and performance investigations across multiple distributed services.

• Design and implementation of event-driven backend services.
• Migration and replacement of legacy processing pipelines.
• Development of replay / rebuild mechanisms for large event datasets.
• High-throughput asynchronous event processing on SNS / SQS.
• Database and query optimisation for PostgreSQL and DynamoDB.
• Design of scalable read / write models and aggregation pipelines.
• Production troubleshooting and operational support.
• Performance tuning and infrastructure scaling.
• Design and stabilisation of integration and system tests.
• Technical concepts, architecture documentation, and cross-team collaboration.
• Support the further development of existing GitLab CI/CD pipelines

Geofence & Wagon Stay Processing

• Algorithm to detect vehicles within geofences (entry, exit, dwell time).
• Event sourcing with guaranteed chronological order within the affected time window.
• Refactored geofence event and wagon-stay processing logic for performance.
• Resolved race conditions and event-ordering problems in distributed services; server-side filtering, aggregation and optimised query pipelines.
• Repair and replay tooling for corrupted or inconsistent movement data.

Fleet Metadata & Mileage

• Modernised the service; migrated storage from DynamoDB to PostgreSQL to improve traceability and accelerate new features.
• Scalable mileage aggregation and replay mechanisms.
• Read / write models and optimised queries for high-volume mileage calculations.

Sensor & Telematics Integration

• Integrated telemetry and sensor processing pipelines.
• Snapshot and state-calculation logic for sensor systems.
• APIs and persistence models for wagon sensor data; data-quality improvements.
• Further development of a service using gRPC for intra-service communication.

Movement Segment Processing & Routing

• Migrated services to new movement-segment event streams.
• Built replay and rebuild tooling for segment correction.
• Optimised throughput and reliability for high-volume event processing.

Condition Monitoring & Wagon Analytics

• APIs and backend services for wagon condition monitoring.
• Brake-wear prediction processing and wagon analytics functionality.
• PostgreSQL views and optimised query models for operational dashboards.

Operational Reliability - First Responder

• Investigated production incidents and distributed-system failures; DLQ analysis, replay and operational recovery.
• Tuned database performance and AWS infrastructure under production load.
• Improved observability, monitoring and operational tooling.
• Supported rollout strategies, monitoring and post-deployment stabilisation.

• Sole technology owner, leading architecture, development, operations, and infrastructure. Leveraging AI to accelerate work in areas like front-end and design.
• Built full-stack solutions (backend, React front-end) with CI/CD pipelines and observability standards, setting the foundation for an engineering organization.
• Delivered AI-driven features using LLMs, automating supplier–buyer matching, lead generation, email campaigns, and reducing manual effort.
• Reduced cloud costs by 90% by migrating the system to an AWS serverless architecture

• 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

• Collaborating with developers, security, and operations teams to align requirements
• Supporting product owners and development teams in deploying logging and monitoring solutions based on the Elastic Stack
• Developing and standardizing log schemas as well as defining practical standards for observability
• Designing and further developing logging architectures for complex, distributed multi-tenant environments
• Connecting application and infrastructure logs as well as security tools and metrics
• Optimizing data flows and modeling for analysis and reporting purposes
• Building automated infrastructures using Terraform/Terragrunt and Ansible
• Maintaining and evolving CI/CD pipelines in GitLab as well as automated development environments in Hetzner Robot
• Operating and automating Proxmox clusters including Ceph storage
• Setting up and running Kubernetes (k3s) clusters on Fedora CoreOS including core services like Vault, OpenLDAP, and HAProxy
• Implementing security-critical infrastructures according to BSI basic protection and securing existing systems
• Supporting the operation of solutions in cloud environments (e.g., AWS)
• Working in agile teams following Scrum and Kanban
• Technologies/Tools: Elastic Stack (Elasticsearch, Logstash, Beats/Elastic Agent, Kibana), Terraform, Terragrunt, Ansible, GitLab CI/CD, GitOps, Proxmox, Proxmox Ceph, Kubernetes (k3s), OpenShift, Helm, Kustomize, Vault, OpenLDAP, HAProxy, Hetzner Robot systems, AWS, Prometheus, Grafana, Syslog Linux/Windows, Docker, NGINX, Apache Security & Compliance (BSI basic protection, SIEM/SOC)