Stream Processing Experts in Germany

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.

Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.

In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.

To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.

Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.

Achievements:

• Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
• Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
• Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
• Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
• Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
• Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
• Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
• Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
• Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
• Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
• Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
• Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
• Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
• Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.

Tech stack:

• Infrastructure as Code: Terraform, AWS CDK, Ansible.
• Containers: Kubernetes on EKS, AKS, Docker.
• Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
• Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
• Backend: Python with FastAPI, also Node.js with NestJS.
• Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
• CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
• Monitoring and Observability: Prometheus and Grafana.
• Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
• ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
• Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
• Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.

• Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
• Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
• Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
• Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
• Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
• Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
• Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
• Created and maintained dashboards in Power BI to provide actionable insights.

• Led engineering execution: roadmap planning, capacity alignment, risk management, dependencies, and delivery tracking.
• Consolidated multiple payroll product lines into a unified SaaS platform on Dynamics 365 Business Central, enabling scalable post-merger operations and reducing operational complexity across the portfolio.
• Restructured engineering and product teams in a remote-first setting across Germany, Austria and Poland, consisting of five cross-functional units: compliance/enabling, platform, DevOps and two stream-aligned teams with total FTE depending on phase of reorganisation.
• Rebuilt the mid-level leadership layer and mentored engineering leaders, establishing a leadership pipeline and strengthening architectural decision-making across teams for scalable growth, delivery ownership and predictability.
• Designed platform foundations and system boundaries using Team Topologies aligned structures, enabling scalable ownership, clear interfaces and parallel development across distributed teams.
• Spearheaded AI transformation by implementing AI-assisted SDLC practices using SpecKit and GitHub Actions for automated, executable specifications, while delivering agentic product capabilities by securely exposing platform data and services to AI agents and copilots via RAG-based retrieval pipelines and MCP-style extensions.
• Drove modularisation of tightly coupled legacy logic into independently deployable services, improving maintainability, testability and architectural clarity while preserving continuity through targeted, low-risk extraction rather than full rewrites.
• Established observability, CI/CD and DevOps governance as platform capabilities, increasing automated compliance gates from 25% to 75% and improving deployment cadence by 40% across 15+ product versions.
• Improved operational resilience using DORA-aligned practices (lead time ↓50%, SaaS MTTR ↓85%), strengthening reliability and reducing support overhead.
• Coordinated engineering recovery for the German payroll platform during a company-wide P0 ransomware incident; restored platform continuity within 72h, validated data integrity, and rolled out hardened runbooks and automated recovery playbooks.
• Responsible for budget compliance and cost oversight in Engineering, with limited P&L responsibility and participating in the annual COGS/OPEX/CAPEX planning cycle.

• Lead Developer / Tech Lead in a large banking group for a complete redevelopment of payment systems
• Rebuilding payment processing in stream processing
• Development and design of cross-border payment stream processing based on Kafka and Kafka Streams
• Development of different input formats for the business team using Drools rules to dynamically adjust validation rules
• Evaluation and comparison of different public cloud providers regarding the databases they offer
• Technologies & Tools: Java 21, Kotlin, Spring Boot, Kafka, Kubernetes, Azure, TDD, JEE, REST, Angular, Spark, Google Cloud, SCDF, PostgreSQL, Drools, Git, S3, MongoDB

• Test management/coordination and defect management for the decentralized feed-in process (Meter2Cash process)
• Test planning and creation of test cases, test execution, and test plans
• Defect pre-analysis and dispatch