Data Warehouse Experts in Berlin

• Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
• Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
• Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
• Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.

Specialize in turning complex data from diverse environments into actionable business value through compelling visual storytelling. I am an expert in generating actionable insights and presenting recommendations to business stakeholders. My technical proficiency in SQL, Python, and leading data visualization tools like Tableau and Power BI allows me to deliver a new generation of self-service tools and analytics services.

• Data Visualization & Storytelling: Created impactful data visualizations and dashboards in Tableau and Power BI, effectively communicating findings and presenting actionable recommendations to C-suite stakeholders and business leaders.
• Stakeholder Management: Built effective working relationships with key business stakeholders, data engineers, and other partners to achieve common data-driven goals and targets.
• Insights & Recommendations: Generated actionable insights from complex data analysis for funnel conversion, marketing performance, and ROI, directly influencing business performance and strategy.
• Data Collaboration & Empowerment: Worked closely with cross-functional teams to support the ongoing data needs of internal partners, helping to optimize internal data processes and workflows.
• BI & Data Expertise: Applied extensive experience in data modeling, data collection, data mining, and analysis to deliver end-to-end analytical solutions from stakeholder discovery to production.

• Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

• Directed the contracts management function for Europe's largest renewable energy portfolio, 10 FTE
• Implemented process automations by architecting scalable, data-driven workflows leveraging AI and traditional automation

Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.

Activities:

• Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
• Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
• Developed automated data pipelines with Python, dbt, and GCP services for different data sources
• Built monitoring and alerting systems for real-time platform monitoring
• Implemented data versioning and quality checks at every layer
• Designed automated test and deployment pipelines in GitLab and Bitbucket

Achievements:

• 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
• Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
• Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
• Migrated 7 database tables with 0 downstream issues
• Removed 100% exposed credentials, eliminated external vendor dependency
• Delivered integration of 3 teams in 1 sprint

• Lecturer for the GenAI Track at the Master School Institute of Technology
• Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation

• Insurance service provider*

Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.

Implementation:

• Architecture and production implementation of an on-premise OCR solution with full data ownership
• Methods for recognizing document structures as the basis for automated further processing
• ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations

Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year

Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL

• Planning and implementing tailored Odoo ERP solutions to support and optimize specific business processes
• Configuring and customizing Odoo modules according to individual customer requirements, including process automation and data integration
• Training and supporting end users and administrators to ensure full use of Odoo features and to enhance user skills
• Providing ongoing post-implementation support, including troubleshooting, maintenance and updates to adapt to new business requirements
• Migrating data and integrating external applications into Odoo environments for a seamless, unified data landscape
• Analyzing and improving existing Odoo systems to boost efficiency and optimize the user experience

• Building a full-stack e-commerce product using AI-assisted development, deliberately returning to hands-on engineering to validate how AI changes software development workflows and team dynamics.
• Exploring VP Technology, Head of Engineering, and Director of Engineering opportunities where hands-on AI experience meets organisational scaling expertise.
• Open to advisory conversations on AI-augmented engineering teams, technology strategy, platform architecture, and organizational design.
• No registered business. No commercial activity.

• Responsible for cross-department requirements management in collaboration with business units, IT, and external vendors
• Gathering, structuring, consolidating, and prioritizing business requirements from various company departments
• Modeling and documenting business processes using BPMN as a basis for transparency and further development of existing solutions
• Analyzing existing business processes and identifying optimization opportunities
• Refining business requirements with regard to feasibility, cost-effectiveness, and impact on adjacent processes
• Facilitating business alignment sessions with relevant stakeholders to refine requirements and support decision-making
• Creating actionable business concepts for IT development
• Supporting implementation, testing, and rollout of new or adjusted solutions
• Assisting in standardizing requirements and processes to build a reliable decision-making foundation

• Establishing Identity & Directory Management as a new (split-off) team & product within the IAM cluster.
• Leading the “Identity & Directory Management” product (8 people) as Product Owner.
• Concept for ‘Digital Identities’, i.e. IDs with n users/accounts and strategy for a modernized product offering.
• Upgrading APIs, migrating to a containerized infrastructure, rolling out international markets & standardized solutions across the REWE enterprise group.
• Tech: OpenText™ (NetIQ) eDirectory & Identity Manager, LDAP, SAP HR/HCM, Docker/Kubernetes/Podman, REST APIs, Microsoft Active Directory & Entra ID, postgresDB, Keycloak, Ansible, Cyberark (PAM), Apache Kafka, Jira, Confluence, Miro.

• Leading coordination between business units and IT
• Coordinating development and testing
• Business analysis and structured requirements gathering
• Specifying functional and technical requirements
• Integrating interfaces to internal systems
• Developing SQL queries and reports
• Documentation in Confluence Result: On-time go-live, structured and agreed project basis, ensuring a coordinated project workflow.

• Projects for federal networks (NdB).
• CR management, EoL change requests, design and documentation according to ITSCM.
• Data center planning.
• Project management and engineering subproject management.
• Software development for virtual server environments according to BSI.

• Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
• Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
• Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
• Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
• Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
• Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
• Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.