Data Vault Experts in Germany

I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.

I delivered following projects and engagements as a freelancer.

• Data Migration of CRM System for AL-FA Objekt Service Gmbh
• Microsoft Software Resales Partnership

I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer

Technical Focus Areas

• Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
• DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
• Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
• MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
• Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.

• 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.

Project, Strategic Consulting – building the Data Strategy and Data Governance Policy for the German branch, client (private bank Julius Bär, headquarters Zurich), March 2026 – present

• Design and negotiation of the data strategy with key stakeholders, including obtaining board sign-off (strategic consulting) – in this context, regulatory advice on data regulations in the EU and specifically for Germany. The data strategy includes: Data Lifecycle Management: data capture, data storage, data usage, data retention policy, data quality incident management
• Definition of milestones and technical feasibility for implementing TOM for the data strategy, data quality checks, metrics, and a metadata inventory to ensure the bank’s compliance with DORA, BCBS239, and MaRisk requirements.

Core project data change, client: (ING Bank, Frankfurt am Main), March – December 2025

• Concept development and solution design for new end-to-end processes including technical interfaces
• Definition of synchronization logic and data flows between legacy and target systems (decommissioning of legacy systems)
• Analysis and validation of data models
• Stakeholder communication with product owners, feature engineers, UX designers, and operational teams for decision-making
• Analytics and impact assessments, e.g. to assess downstream effects and regulatory requirements
• Documentation and comments on technical and business requirements to support implementation in agile squads

Project digitalization of a user group, client: (ING Bank, Frankfurt am Main), as Interim Product Owner, Jan 2025 – present

• Co-shaping key decisions on data architecture and process logic in the context of historized data and user login functionality
• Development of business solution concepts for migration to the target system, including system integration and data flows
• Support with analytics and impact analyses, especially regarding the ability to provide information to law enforcement authorities
• Active coordination with stakeholders from different squads to support decision-making and ensure regulatory requirements are met
• Creation of test scenarios for operational teams and backend systems in the area of API management using Postman and Bruno.

• 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

Engaged as an independent BI consultant for a FinTech SaaS platform, building and maintaining the end-to-end Microsoft Azure and Fabric analytics stack behind a multi-tenant accounting product used by business owners to track their own financial performance.

• Partnered with finance stakeholders to translate complex accounting workflows into real-time BI dashboards (P&L, Cash Flow, Balance Sheet), helping 2000+ business owners track cash flow and overall financial health effortlessly.
• Developed 10+ interactive dashboards, giving each business owner personalized, real-time visibility into their own sales performance, accounts receivable aging, and operational costs.
• Enforced strict enterprise data governance by implementing dynamic Row-Level Security (RLS) via DAX to isolate each business owner's data within the shared multi-tenant model.
• Maintained and extended an Azure ELT pipeline (Azure Data Factory, Azure SQL Database, Azure Synapse) that automated data ingestion and transformation.
• Contributed to a team-wide platform migration from Azure to Microsoft Fabric to optimize capacity licensing costs.
• Designed and implemented a Microsoft Fabric ELT pipeline (Lakehouse, Pipelines, PySpark) that automated data ingestion and transformation, replacing the prior Azure pipeline.
• Modeled cleansed data into a Star Schema within the data warehouse using T-SQL, powering the Power BI Semantic Model.
• Leveraged AI tools, including Copilot and Claude, as development partners across the entire ELT pipeline, from data ingestion to KPI reporting and visualization.
• Supported agile delivery processes, using Jira to track development tasks and Confluence to maintain technical and project documentation.

• Led the migration of the existing DWH, including HR data, application processes, project business and time tracking, from an MS SQL Server environment to Azure Databricks
• Refactored existing data models and ETL processes and extended them with new objects and functionalities
• Used Azure Databricks (Unity Catalog, Notebooks, Spark SQL, Databricks Dataflows) and Power BI
• Technologies: MS SQL Server 2019 (SSMS, SSIS), Power BI, Delta Lake, Azure Databricks

• 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

DWH for customer and financial data

• Expansion of the DWH to include new data sources
• Report development
• Data quality management

Methodology: Scrum

Tools: Atlassian Confluence & Jira

Databases: Microsoft SQL Server

Programming languages: SQL, T-SQL

ETL: Microsoft SQL Server Integration Services (SSIS)

Frontend platform: PowerBI, Microsoft Reporting Services

• Consultancy Databricks Lakehouse Platform Architecture
• Migration of existing manufacturing data services to Databricks. Existing services are running on various platforms & tools and are unified on target platform
• Implementation of new Data & AI use cases on Databricks platform (e.g. connecting new systems, building AI Agent prototypes, …)

Tech Stack: Databricks, Azure, PySpark, Python

• Analysis of business requirements
• Integration of different data sources such as ERP systems, production systems, and external data sources
• Implementation of load processes and processing logic with MSSQL
• Data modeling and optimization of data models
• Setting up data quality management incl. data profiling with dynamic programming
• Support in designing and establishing data governance, especially in the areas of data quality management and data protection
• Support in developing BI solutions with Tableau to help decision-making processes

• Establishing the Microsoft Fabric platform with the company as the central data warehouse and reporting system.
• The company migrated several operational systems including D365 to new version. The new data warehouse is based on a Fabric/Power BI architecture with Azure cloud Entra Authentication.

Tasks Performed:

• Full administrative responsibility for the Fabric platform F64 and F32 capacities, as well as two F8 capacities for development and prototyping.
• Integration of Fabric with Microsoft Entra.
• Data modeling for the data warehouse bronze, silver and gold layers using data modeling tools.
• Star Schema as well as entity relationship modeling.
• Data modeling for data marts and for dimensional modeling and Power BI (semantic model).
• Design of data models for Microsoft SSAS multidimensional and tabular OLAP cubes using DAX and MDX.
• Definition of design patterns for the ETL team for loading OLAP cubes (Tabular and MD), star schemas and data vault structures.
• Planning and managing team of 4 ETL and reporting developers.
• Performance Tuning of Power BI reports, particularly the semantic layer, as well as the Fabric notebooks
• SQL Performance Tuning.
• DB Design for Azure SQL.
• Reporting directly to project senior management.

Label: Power BI, Fabric, Analysis Services (multidimensional and tabular), D365, SSIS, Tabular Editor, SQL Server and Azure SQL, TOAD Data Modeler, DBSchema, Visual Studio Code, DAX Studio, SSMS, Visual Studio, Jira and Confluence.

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

I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.

Main Tasks:

• Sprint planning and feature preparation
• Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
• Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
• Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
• Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
• Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
• Manage production releases and execute live data migrations for enterprise customers
• Define engineering standards and architecture patterns for the team

Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL

• Designed a future-proof client database for marketing purposes
• Analyzed requirements, designed, and modeled an entity-relationship model
• Consolidated and optimized a client file from various data sources for targeted marketing campaigns
• Worked closely with marketing and IT in an agile environment to iteratively develop the solution
• Technologies and methods: MS Office (mainly Excel), MS Dynamics CRM, MS SharePoint