Snowflake Expert in Munich

• 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

PMO & Project Organization Blueprint for Restructuring

Renewable Energy / Solar Equipment

Technologies / Methods: PMO setup, KPI tracking, project organization, Jira, Confluence

• Developed measures to improve management control during a restruct...

• Led enterprise reporting modernization by migrating legacy SSRS reporting solutions to Power BI, supporting 500+ business users while ensuring full GDPR/DSGVO compliance.
• Designed and optimized Power BI and Microsoft Fabric semantic models using star schema, dimensional modeling, advanced DAX, and performance optimization techniques, reducing query latency by 25%.
• Delivered 20+ executive and operational dashboards featuring KPI scorecards, drill-through, bookmarks, and row-level security, improving reporting efficiency by 20%.
• Enabled self-service analytics through governed Power BI datasets, dataflows, and gateway architecture, increasing business-led reporting adoption by 35%.
• Configured an incremental refresh policy and query folding for a 50+ million row sales dataset, reducing daily report refresh times by 85%.
• Deployed automated ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, and Snowflake, reducing reporting delivery timelines by 40% through workflow automation.
• Spearheaded Microsoft Fabric analytics modernization initiatives including lakehouse architecture, OneLake integration, and centralized data platform development, reducing data latency from 2 hours to 20 minutes.
• Translated business requirements from 15+ stakeholders into scalable Power BI semantic models and dashboards, improving reporting consistency and reducing ad-hoc reporting requests by 25%.
• Applied Microsoft Copilot and generative AI tools to accelerate SQL development, DAX authoring, technical documentation, and testing activities, reducing development effort by approximately 15 hours per week.

• 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

• Partner directly with C-level leadership (CEO, CAIO, CTO) on architecture, OKR strategy, and cross-team roadmap prioritization, translating strategic goals into structured engineering requirements.
• Surfaced and mapped technical debt across the entire organization with C-level leadership and co-defined a prioritized remediation strategy, balancing debt paydown against feature delivery.
• Led code reviews and technical standards across the team, fostering a mentor-first environment with two-way feedback dialogue — pairing on complex pipeline work and unblocking junior engineers on async architecture patterns.
• Re-architected the AI companion's core processing pipeline from synchronous to asynchronous with a queue-based worker architecture, enabling horizontal scalability and cutting upload processing time ~4x (from ~22s to 5–10s) while improving response accuracy.
• Designed an AI-driven document intelligence workflow with automatic multi-document classification, per-document summarization, and relevance guardrails for the patient care journey.
• Built a unified patient memory system (short- and long-term context) bridging the document vault and chatbot into a single bidirectional, context-aware platform.

• 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

• Designed, developed, and configured scalable data applications aligned with business processes and technical requirements.
• Architected scalable, cost-effective data architectures leveraging Snowflake across AWS, Azure and GCP, integrating dbt for data transformation and modeling.
• Built and maintained robust ETL Data Pipelines, ensuring high data quality for seamless migration and cross-system integration.
• Demonstrated strong expertise in SQL & Python with extensive experience in data modeling, ETL/ELT pipeline development, and streaming data processing; proficient in Git-based version control, CI/CD practices, and testing frameworks, with solid knowledge of data quality, observability, cost optimization, security, and data governance principles.
• Led multiple data migration initiatives from SAP HANA to Snowflake using a modular dbt framework.
• Designed and maintained end-to-end data transformation workflows using dbt on Snowflake, implemented layered data models, optimized performance, and ensured high-quality data delivery for business intelligence and reporting.
• Managed development, QA, and production deployments through structured version control and release management using GitLab.
• Integrated and centralized data from multiple sources including relational databases, flat files, Excel, and large-scale systems into Snowflake.
• Applied strong expertise in Sales, Marketing, HR, and ERP data domains, developing and maintaining relevant KPIs and reporting solutions.
• Collaborated with cross-functional teams to deliver end-to-end data solutions on schedule through proactive issue resolution and effective coordination.
• Administered the Snowflake sandbox environment for Data Engineering division.
• Trained colleagues transitioning into data roles on Snowflake and provided technical guidance and mentorship to junior team members.

AI Assistance and Target Vision for Partially Autonomous Energy Portfolio Management

Building an AI control layer directly on the up-to-date daily live portfolio of a large energy provider — not as an isolated pilot, but as an operational extension of the existing DB1 and portfolio management. The goal is the gradual development from assistance through monitoring/alerting to analysis agents with Human-in-the-Loop approvals, supplemented by a role-specific System of Engagement alongside the BI System of Record. At the same time, the business case, target vision and management pitch compared with static monthly reporting are being developed.

• AI control layer: Design and build on the existing live portfolio data product (several million contracts) — development stages assistance → monitoring/alerting → agents with Human-in-the-Loop approvals.
• LLM-supported data analysis: Semantic queries, SQL/tool integration and additional RAG components based on portfolio, plan-versus-actual and data quality data (Azure OpenAI, Snowflake), with drill-downs to individual contract level.
• Analysis agents: Multi-stage agents for variance and driver analyses of churn, price adjustments, volumes and procurement costs.
• Views concept: Role-specific interfaces for business units, management and C-level as a System of Engagement alongside the BI System of Record.
• Business case & pitch: Target vision and cost-benefit argumentation compared with static monthly reporting.
• LLM setup (privacy & security): Coordination with IT Security and Data Protection — EU region, data separation and approval processes.
• Agent architecture: Multi-stage agent pipelines (analysis → validation → summary) with documented data sources, tool calls, review steps and source references for each statement.
• Data foundation: Built on the up-to-date daily DB1 data product (Snowflake, dbt) — portfolio, plan-versus-actual and data quality metrics as the common basis for all AI analyses.
• Guardrails & evaluation: Evaluation and approval processes for LLM responses relating to management-relevant statements — test sets, metrics and human review.
• Prototyping & validation: Iterative validation of agent responses with the business unit — test question catalogue, feedback loops and response quality for each release.
• Roadmap & development stages: Detailed stages from assistance → monitoring/alerting → partially autonomous management, including transition criteria and governance for each stage.
• Integration: Integration into the existing BI and data landscape — BI remains the System of Record, while the AI layer provides interactive drill-down paths as the System of Engagement.
• Enablement: Enablement of business users — prompting guides, training and an operating model for ongoing use.
• Management: Coordination of business units, Data Engineering, IT Security and Data Protection.
• Change Management: Communication and expectation management with business units and management throughout the development stages.

Results:

• Built on an existing up-to-date daily data product with several million contracts
• Established an LLM setup coordinated with Data Protection and IT Security in the EU region, including data separation and approvals
• Defined three development stages through to partially autonomous management
• Designed role-specific views for business units, management and C-level
• Developed the business case and management pitch for the development stages
• Established an iterative response-quality validation process with the business unit
• Designed the operating model for assistance operations and initiated validation

Stack: Azure OpenAI, Azure AI Foundry, Snowflake, dbt, React, TypeScript, Entra ID, RAG, Agentic AI, analysis agents, Human-in-the-Loop, Prompt Engineering, LLM Evaluation, LLMOps, GDPR / EU region, Azure DevOps, Python, SQL, Change Management

Large Insurance Company – Consultant Wiesbaden: Consulting support for the introduction of an integrated planning and performance management framework (operational, financial, customer) to enhance customer-centric transparency, decision-making quality, and steering capabilities across all lines of business within an insurance organization:

• Analysis of existing processes, reports, KPIs, and KPI calculation methodologies
• Design and introduction of new, standardized customer KPIs (gross/net), as well as key steering metrics with consistent linkage across all lines of business
• Recalculation, validation, and plausibility checks of KPIs based on existing and newly integrated data sources
• Conceptual support for the development of an integrated reporting and performance management setup
• Execution of customer insights analyses to identify patterns and anomalies within customer data clusters

Large retail company – Consultant in Karlsruhe: Advisory services for the setup and step-by-step implementation of an internationally deployable RELEX solution in the supply chain management environment:

• Advising overall and sub-project management on methodology, project setup and steering (e.g. agile approach, Jira configuration, RELEX phases, Jira Structure PPM)
• Strategic-operational consulting for the introduction of RELEX including best practices
• Support in defining overarching goals and requirements (2-year target picture)
• Guidance in scoping a relevant supply chain network segment for the project
• Development of a roadmap for iterative, incremental RELEX setup and rollout
• Assessment of project dependencies (interfaces, configurations, etc.)
• Advice on prioritized implementation of business requirements and data interfaces
• Support in test planning (data validation, system testing, UAT)
• Consulting on internationalization, change management, training, and knowledge transfer
• Stakeholder advisory and alignment activities between the client, implementation partner, and RELEX

Insurance company – Management Consultant in Munich: Analysis, consulting and support for the optimization of a large-scale business and IT transformation. Focus on strategically important programs and modernization projects in the area of Managed Services Operations and processes:

• Review of project plans and deliverables; analysis of programs and projects (e.g. cloud approach, process standardization, system integration, roadmaps)
• Identification of technical, functional and personnel risks and challenges; development of content-related measures and alternative solutions
• Proposal of quality improvements for program and modernization efforts
• Sparring partner and professional, technical, structural and organizational consulting for project and program management

Large retail group – Management Consultant & Stream Lead in Cologne: Consulting, process, project and product management for the introduction and implementation of a large strategic program in the field of advanced analytics, assortment and space management:

• Setup, test and rollout of a new space planning, automation and optimization product based on the existing cluster-based merchandising approach
• Definition and setup of new processes and transformation and change management measures for the new store-specific merchandising approach
• Collaboration with Advanced Analytics and IT (internal and external) for software implementations, automations, extensions and interfaces
• MVP approach and piloting in phases with gradual rollout (pilot with 80 stores, region with 500 stores, national level with 4000 stores)

Large retail company – Agile Coach & Change Agent in Cologne: Agile coach, OKR master and facilitator for the introduction of the OKR approach in a large strategic digitization program for retail stores:

• Coaching of the core team with topic managers and team leads
• Introduction to the OKR topic and setup of the OKR cycle
• Establishment of the OKR approach in teams and on a cross-team level

Delivery and logistics company – Management Consultant in United Kingdom: Consulting and coaching in the restructuring of the Data Analytics department:

• Analysis of current challenges
• Definition of overarching goals
• Development of a proposal for a new team structure
• Identification of required competencies, skills and responsibilities
• Advisory and alignment on communication and change management strategy

• AI-driven marketing to boost SME growth with smart automation
• Data-led strategy to maximize ROI through targeted campaigns

• Agile development of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
• Extraction of context-relevant content based on user profiles & competency dimensions
• Personalized delivery of learning content to improve sales performance
• Close coordination with sales teams & stakeholders to validate features
• Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
• Integration into existing tools & CRM systems for smooth adoption
• Technologies used: Python, OpenAI, DB tech such as PostgreSQL, CI/CD for AirFlow DAGs, FastAPI

• Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
• Designed and implemented multi-agent AI workflows for financial and trading applications
• Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
• Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
• Led system architecture decisions across model selection, orchestration, state management, and deployment

• Topics: Business Analysis, Data Privacy, Data Management, Stakeholder Management, Conceptualization
• This project focuses on developing and implementing a strategic approach for data deletion in SAP systems. The goal is to identify the relevant data and structures during system migration to ensure both data privacy and IT system efficiency. At the same time, downtime should be minimized and regulatory requirements met.
• Development of a comprehensive approach for data deletion in SAP systems, considering data privacy and business requirements.
• Ensuring efficient and structured data transfer to the new system.
• Optimizing system efficiency and reducing downtimes during migration.
• Creating functional and technical concepts to ensure compliant and sustainable data management.
• Topic preparation: Detailed study of the "data deletion" area to lay the foundation for a structured data migration.
• Definition of project structure: Setting roles, interfaces and the project's organizational structure.
• Regulatory requirements: Analysis of data privacy regulations and business requirements to define deletion criteria.
• Approach: Developing possible scenarios and methods for data cleansing and deletion.
• Deletion concepts: Creating functional and technical deletion concepts that structure the implementation and provide clear guidelines.
• Setting deletion criteria: Defining which data and structures to delete or transfer.
• Responsibilities: Clarifying responsibilities within the project team and among stakeholders.
• Analysis of ongoing activities: Identifying and collecting existing activities in the "data deletion" area.
• Effort, cost and timeline planning: Creating estimates for resources, effort and budget.
• Implementation initiatives: Developing and executing concrete measures to apply the defined deletion strategies.
• IT system efficiency: Analyzing the existing IT infrastructure to identify optimization potential for data deletion and transfer.
• Technology trends: Evaluating new technologies and tools that can support the data cleansing process.
• Cost-benefit analysis: Assessing the financial impact of data cleansing and the introduction of new solution approaches.
• Risk management: Identifying potential risks during implementation and developing appropriate mitigation measures.
• This project lays the foundation for a sustainable and compliant data transfer to a new SAP system. With a clear approach to data deletion, it meets data privacy requirements, reduces downtimes and increases the efficiency of the new system. The results and recommendations will help companies develop a future-proof data strategy that meets legal and business needs.

• Functional responsibility for the central pricing application Periscope by McKinsey in an international, cross-country retail environment.
• Management of change and run phases for the pricing application and the connected data and system processes
• Gathering, analysis and prioritization of functional pricing requirements and change requests from local and global business units
• Translating existing calculation logic and new requirements into functional concepts and acceptance criteria
• Automated validation of price calculations, pricing rules and master data across very large data volumes using custom Python scripts: logic, mapping and data errors are identified before prices go live
• Product expert and central contact for functional and system-related questions
• Rollouts across countries and sales channels, including testing, documentation and knowledge transfer
• Collaboration with external service providers and the software vendor