Snowflake Experts in Munich

• 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

• Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
• Used multiple experts (OpenAI models) collaborating during document drafting
• Extracted useful information from the knowledge graph
• Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
• Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
• Deployed initial application as a Streamlit app

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

• 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

• 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.

Project description: Design and step-by-step implementation of a central, multidimensional controlling platform based on IBM Planning Analytics to optimize and automate internal company planning and reporting.

Business & technical consulting: Analyzed the provided operational base data and proactively advised the controlling team on cube design best practices and optimal dimension structures.

Data integration & ETL: Designed and developed robust TurboIntegrator processes for automated data loading, transformation, and dimension maintenance.

Logic & business implementation: Implemented complex business logic and calculations with high performance using TM1 business rules (including efficient skipping/feeding).

Development of a Python HTTPS server to integrate an input widget in IBM PAW, enabling direct data write-back to TM1 cubes via the TM1 REST API.

Reporting & analytics: Created dynamic, user-friendly reports and dashboards for management and specialist departments.

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

• 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.

• Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
• Performance Optimization of Data Ingestion of ETL Pipeline
• Development of Data Validation using Great Expectations
• Leading of the data migration for two sources exchanges
• Data Modeling in AWS Redshift

MLOps

• Model inference implementation by mlflow and AWS SageMaker
• Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
• Implementatino of Model Registry and artifactory using mlflow
• Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
• Feature importance using mlflow

Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy

• Gathering requirements from business units and stakeholders
• Organizing and running workshops
• Creating and supporting user stories (from refinement to rollout) and mapping
• Facilitating Scrum ceremonies
• Identifying and analyzing optimization potentials like the REWE Pick&Go app
• Moderation and communication with service providers
• Requirements engineering in connection with external systems
• DWH/BI solution: loyalty reporting with MicroStrategy
• ITIL (framework for IT service delivery)
• Quality assurance (quality gates) based on ISTQB

• Developed web scraping scripts to extract and parse over 5000 product data from the Zalando website.
• Performed ETL processes using Apache Spark in CDSW, loaded the data into the Hadoop ecosystem (HDFS), and managed data using Hive and Impala.
• Implemented machine learning algorithms, achieving 85–90% accuracy on multi-class product classification.
• Integrated Zalando's product and price data into the dashboard with Otto and Takko for interactive visuals.

• Optimized SQL data pipelines for clean insights.
• Analyzed and visualized trends with Python.
• Improved dashboards and automations.
• Worked with Google Sheets, GCP, Snowflake, dbt, Spreadsheets/Excel, Databricks, PySpark, and Fivetran.