Data Visualization Expert in Munich

• Taught ML, BI, and Big Data to Master's students.

Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

• Leading enterprise BI transformation across Power BI & Microsoft Fabric, delivering scalable data models, automated reporting, and high-performance analytics solutions for commercial and operational leadership.
• Building and optimizing Power BI Dataflows, Fabric Lakehouse datasets, semantic models, and automated reporting pipelines to improve data scalability, governance, and reporting performance.
• Driving dashboard modernization and KPI governance by translating complex business requirements into executive-level insights, interactive visualizations, and decision-ready analytics.
• Designing end-to-end Microsoft Fabric architectures integrating data ingestion, transformation, virtualization, and enterprise reporting across cross-functional business domains with SAP BW to Qlik to Power BI migration.
• Delivering AI-enabled reporting capabilities, threshold-based alerting, and automation frameworks within the Power BI ecosystem to accelerate business decision-making.
• Partnering with commercial leadership, analytics teams, and IT stakeholders to standardize KPIs, optimize BI strategy, and deliver scalable, business-critical reporting solutions.
• Recognized for combining strong stakeholder leadership, technical architecture expertise, and business-driven analytics to deliver impactful enterprise BI transformation initiatives.

Sub-project lead in an agile IT environment

Leading a software development team (10 employees)

• Leading a software development team with 10 employees
• Budget responsibility approx. 3 million euros per year
• Conducting employee and job interviews
• Responsibility for resource planning (budget and employees)
• Digitalization of a web interface
• Regular reporting to customers and stakeholders (C-level)
• Product management: developing and implementing product strategies; defining requirements to guide product development
• Structuring product development & continuous testing efficiently to quickly incorporate feedback & compliance points
• Close collaboration with stakeholders & various teams to understand requirements, set priorities, ensure rollout, and increase user-friendliness
• Project experience in an energy industry company, including the functioning of energy markets, the basics of energy generation and distribution, as well as current challenges and developments in the industry
• Project experience in the railway sector - including the structure and operation of railway systems.
• Project experience within the 16.7 Hz traction power sector incl. knowledge of regulatory processes
• Formulating the sub-project charter and the project profile according to the project standard together with the project management
• Controlling service delivery according to the project charter
• Planning and controlling human resources including budget planning for the sub-project
• Ensuring the work capacity of the sub-project participants, e.g. by requesting access rights and documents for the sub-project team
• Planning and control for the respective sub-project (time, scope, budget) including the creation of necessary documents
• Alignment with the business department and Product Owners to plan the sub-project scope and the corresponding implications
• Executing milestone planning according to the agreed process model
• Effort estimation and support of sub-project controlling as well as overall project controlling in forecasting and planning
• Reporting sub-project status (according to status slide template) including progress (according to progress measurement)
• Analyzing, preparing, and addressing action and decision requirements
• Escalating sub-project issues to the project management
• Creating and managing Change Requests (CR)
• Quality assessment of completed task packages from a project management perspective (time, scope, and budget)
• Implementation management of the overarching guidelines and requirements in the sub-project, supported by cross-functional roles on the functional and technical side.
• Identifying, managing, documenting, and communicating opportunities, risks, and dependencies within and across sub-projects
• Supporting project management in creating the overall project plan and overall status reports
• Preparing and managing commissioning including the required documents and protection needs assessment

Scrum Master in an agile IT environment supporting a software development team in the full-stack area (7 people)

• Close collaboration with stakeholders & various teams to understand requirements, set priorities, ensure rollout, and increase user-friendliness
• Implementation of the selected Scrum method, including the required events, meetings, artifacts (product backlog, sprint backlog, burndown charts), metrics, as well as conducting required training in an agile IT environment
• Preparation of Scrum events such as Daily, retrospectives, Review, Sprint planning, estimation, and refinement (agile way of working)
• Moderation of retrospectives and elimination of obstacles to move the team and product forward
• Preparation of action items and retros via whiteboard and Figma
• Application and improvement of agile methods such as Kanban, Scrum, SAFe, or Scrum of Scrums
• Tracking the "Definition of Done" and "Definition of Ready" for all work results of the development team
• Advising on information flow and communication improvements between Product Owner and project team
• Experience with automation, validation such as mandatory field checks, notification functions, creation of filters, boards, and reports via JQL, and setting up cycle time
• Use of Jira & Confluence
• Setting up a progress measurement for project management to transparently map planned vs. actual team capacities and actual efforts for user stories
• Measuring team progress: creating team KPIs: "velocity", "burn-down chart", "throughput", "costs", "quality", "scope"
• Evaluation of agile metrics, process management, and process modeling methods (BPMN, EPC, UML)
• Communication and moderation know-how
• Methodological competence proven in conflict discussions

• Spearheaded efforts to enhance product consistency and achieve significantly better usability, ensuring a more intuitive experience
• Partnered with the founders to define a new go-to-market strategy through product discovery
• Directed the process of creating a new company website from concept and wireframes to design and final implementation in Webflow
• Responsive Webdesign
• WCAG
• Corporate Branding
• Product Discovery
• MVP
• Product Design
• User Experience
• Rapid Prototyping

• 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

• Designing the audit logging framework
• Implementing APIs for developers to integrate in their codebase
• Implementing ingestion pipeline, database query layer and UI for browsing the audit events
• Improving stability and reliability of the backend system

• Designing AI-first workflows that integrate AI into existing product experiences
• Developing UX concepts for prompt management, reusable and combined prompt workflows, tagging, search and information organisation
• Creating user flows, wireframes, prototypes and high-fidelity interfaces in Figma
• Defining reusable UI patterns, components and interaction logic for scalable products
• Exploring human-AI interaction patterns with emphasis on user control, transparency and manageable cognitive load
• Translating product requirements and technical constraints into developer-ready UX/UI specifications

• Defined the vision, strategy, and roadmap of AI-powered analytics products, ensuring they met the business needs of Sales, Marketing, Finance, and executive teams across Europe.
• Collected business requirements, prioritized AI product features, and led Agile development of forecasting and analytics solutions. Defined product specifications, user stories, and acceptance criteria to ensure successful delivery.
• Collaborated with business stakeholders, Product Owners, data scientists, ML engineers and software engineers to transform AI models into scalable business products and integrate AI insights into operational workflows.
• Designed and implemented Generative AI solutions leveraging Large Language Models (LLMs) to automate reporting and enable natural-language querying of enterprise data, reducing manual effort by approximately 30%.
• Defined product goals and success metrics, tracked product performance and user adoption, and continuously improved the product based on user feedback and business results.
• Established data governance, master data quality and reporting standards across SQL, BigQuery and Power BI environments to ensure reliable, secure and scalable analytics.

• Data Engineering: Design and implementation of scalable data pipelines
• Legacy migrations to Microsoft Fabric (Lakehouse, Dataflows Gen2, Pipelines)
• BO Universe migrations to MS Fabric / Semantic Models / Power BI
• Taskforce for data-driven transformation projects involving Azure Fabric / Oracle / MSSQL

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.

• Architect end-to-end AI/ML solutions, with a strong focus on advanced LLM applications, autonomous agentic AI workflows using MCP toolset, vLM embedding computations, and robust agentic RAG pipelines to drive real-world business impact for clients across diverse sectors.
• Designed intelligent agentic AI workflows to automate knowledge retrieval and reasoning for B2B clients, accelerating business impact.

• Built a Python pipeline that converts lectionary web content into structured daily JSON, applying liturgical-calendar rules for accurate weekday and Sunday coverage.
• Shipped a read-only FastAPI REST API with shared Pydantic models and delivered a Kirundi-first web client for browser and mobile use.
• Owned the data layer and backend architecture, collaborating closely on system architecture and interfaces while automating refreshes with GitHub Actions and validating the ETL with pytest.
• Impact: Created a reliable, API-driven source of truth for daily Bible readings in Kirundi, enabling consistent access to previously unstructured content.

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
• Development of feature store and data synchronization between environments
• Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
• Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
• Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
• Development of Copilot Agent Skills