GitLab CI/CD Experts in Frankfurt

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

• Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
• GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
• AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner

Independently built a data platform with cross-system process monitoring.

• Designed the Bronze/Silver/Gold architecture and Python-based pipelines for diverse operational sources.
• Cleaned, harmonized, and validated data; processed and stored it in Parquet format.
• Tracked data history and built business-focused fact and dimension models for traceable reporting.
• Automated data delivery with Python and Power Automate (desktop and cloud); used SharePoint for archiving and data access.
• Developed the Power BI solution from data preparation through to visualizing process statuses, work progress, and deviations.

Technologies: Python, SQL, Parquet, Power BI, Power Query, Power Automate, SharePoint, Copilot

• Development and optimization of the RTL+ frontend application for SmartTV and set-top box platforms with React and Next.js.
• Key role in the technical coordination of developers within the team and in coordinating implementation.
• Central interface to adjacent teams to simplify development processes and improve cross-team alignment.
• Improved frontend performance, stability, and rendering behavior on low-powered devices in a restricted runtime environment.
• Implemented a frontend testing strategy with Jest, React Testing Library, and Playwright.
• Implemented accessibility improvements according to WCAG 2.2 and WAI-ARIA.
• Integrated Didomi Consent Management as a contribution to increasing ad monetization on streaming platforms.
• Used AI-supported engineering workflows with Cursor for structured implementation, refactoring, and faster problem solving.

Technologies used: React, Next.js, TypeScript, JavaScript, GraphQL, Apollo Gateway, Zustand, Tailwind CSS, Styled Components, React Testing Library, Playwright, Jest, HTML5, CSS3, AWS Lambda, EC2, CloudFront, S3, GitLab CI/CD, NX, Cursor

Statistical Forecasting Algorithm

• Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
• Migration from R/On-premise to Python/Snowflake
• Productionalization on Snowflake in cooperation with data engineers & DevOps

Monitoring Dashboard

• Data engineering for preparation & provisioning of necessary data/resources on Snowflake
• Development & deployment of a Streamlit dashboard in Snowflake

ML-based Probabilistic Forecasting on Vertex AI

• Development of a ML-based probabilistic forecasting algorithm from scratch
• Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI

Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow

• Hands-on solution architect
• Digital product development - SaaS
• Strategic consulting on software architecture, cloud migration, and DevOps processes
• Coaching SE developers and IT architects

• Designed a network management strategy focused on NetDevOps principles and model-driven telemetry using YANG data models
• Conducted NETCONF/YANG workshops for L2VPN/L3VPN services over SRv6
• Automated configuration deployment in the testing environment with Ansible
• Developed end-to-end service measurement concepts with IPSLA and the Telegraf-Prometheus-Grafana stack
• Created the dual-stack architecture design (router + switch + firewall) including a detailed test plan; planned, executed, and documented acceptance tests for the management network
• Performed BSI IT baseline protection checks according to the IT-Grundschutz catalog (needs assessment, modeling, risk analysis, deriving measures)
• Protocols: SRv6, IPv6, MPLS, BGP, ISIS, OSPF, NETCONF/YANG, model-driven telemetry, IPSLA
• Systems: Cisco Crosswork Network Controller, SolarWinds Orion, Grafana, InfluxDB, Telegraf, Ansible, Git
• Components: Cisco NCS router series, Cisco Catalyst switch series, Cisco Firepower firewall series

• Database design with Oracle DB, JPA and Flyway considering high performance requirements
• Analysis of multiple architecture drafts and identification of technical risks
• Design and implementation of various interfaces and integrations, especially REST APIs and Kafka topics
• Setup of Elasticsearch for monitoring and analyzing log data
• Establishment of an iterative work approach in the project team facing complex business requirements

• Platform established and running stably; deliberately reducing my involvement to refocus on an engineering mandate in the financial sector.
• Built an own SaaS learning platform from the ground up and scaled it to over 20,000 users (over 6,000 courses sold, B2C and B2B); end-to-end ownership from development through infrastructure to operations.
• Built a lab environment that provisions an isolated Linux container per user (Docker, Traefik, Go), including automatic provisioning and a dedicated subdomain per user.
• Integrated LLM features into the product and accelerated development end-to-end with AI-assisted workflows (Claude Code, Codex); CI/CD with automated tests.

• Intensive language course with a focus on professional language content.

• Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
• Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
• Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
• Use of Scrum and Kanban methods.
• Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
• Development of new plugins and add-ons needed on current infrastructure.
• Database support.
• Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
• Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
• Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
• Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
• Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
• Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
• Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
• Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
• Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
• Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
• Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
• Automated system provisioning and deployment using CloudFormation templates.
• Configuration of IAM roles, policies and permissions to ensure secure access control.
• Patch management, backup automation and disaster recovery setup on AWS infrastructure.
• Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
• Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
• Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
• Configuration of AWS CloudWatch to monitor application performance and system events.
• Planning and execution of migration of on-premises applications to AWS cloud platforms.
• Deployment of containerized applications using Docker and Kubernetes in AWS environments.
• Deployment of internal software packages between availability zones using AWS CodeDeploy.
• Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.

• Central build and delivery for 20+ applications, 100+ pipelines/day, 700+ GitLab projects
• Build pipelines for Go, Java and JavaScript
• Provisioning of 100+ components
• Quality assurance via GitLab Code Quality and SonarQube
• Checks for dependencies, licensing and vulnerabilities
• Release creation via Jira and ServiceNow
• SBOM, Supply Chain Security, distroless images
• PoC GitLab Runner: Nomad vs. Kubernetes
• Technologies: Artifactory, buildah, GitLab Premium, Go, Gradle, Jenkins, Mend, Podman

• Design, develop, and maintain automated tests using Playwright with Python scripts, Typescript and Spring Boot
• Create test classes annotated with Spring Boot Test
• Create test scripts to cover functional, regression, and integration testing
• Ensure test automation coverage for web applications
• Collaborate with developers to identify testable requirements
• Troubleshoot automation test failures and resolve issues
• Integrate automated tests with CI/CD pipelines (e.g., Jenkins, GitLab CI/CD)
• Monitor and report test automation metrics
• Implement test data management and mocking strategies
• Ensure test reliability, stability, and maintainability
• Experience with containerization (Docker) and orchestration (Kubernetes)

• Built a cross-platform mobile application using React Native that interacts with IoT devices via RESTful APIs
• Implemented state management with Redux and integrated IoT-specific components for smooth data visualization and device control
• Developed RESTful APIs using Node.js and Express, utilizing MongoDB for scalable data storage
• Facilitated secure communication between the mobile app and IoT devices, enabling device onboarding, status monitoring, and control commands
• Implemented real-time data synchronization and optimized data flow to minimize latency