Azure Synapse Analytics Experts in Germany

• 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.

Enterprise Analytics Modernization – Germany-based enterprise reporting platform for operations and management analytics, used by 1,000+ internal users across multiple departments.

• Lead end-to-end Power BI and Microsoft Fabric reporting initiatives, delivering scalable dashboards and semantic models supporting daily operational and strategic decisions, achieving 30% faster decision turnaround and 25% reporting efficiency gains.
• Designed unified enterprise datasets using Microsoft Fabric Lakehouse and OneLake, automating historical data processing and reducing manual reporting effort by 40%.
• Built and maintained automated ingestion pipelines using Fabric Dataflows Gen2 and Data Pipelines, improving data refresh reliability to 99.8% uptime and ensuring consistent data quality.
• Implemented enterprise reporting governance, including Row-Level Security (RLS), workspace strategy, deployment pipelines, and documentation, increasing dashboard adoption by 35%.

Technologies used: Power BI, Microsoft Fabric, DAX, Power Query, SQL, Azure Data Fundamentals, Semantic Modeling, RLS, Agile

Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.

Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows

• Development of Power BI reports, measures, and KPIs for sales, purchasing, materials management, warehousing, and production
• Analysis of the proALPHA database (Progress OpenEdge) and high-performance SQL queries for extraction and transformation
• Data models, ETL processes, and pipelines including automated refresh via Power BI Service and Data Gateway
• Gathering requirements and implementation in close coordination with the specialist departments

Selected projects:

• Sales report: revenue, incoming orders, and order status
• Purchasing report with automated purchase suggestions (Power Automate)
• Monitoring of missing parts for production and materials management
• Production bills of materials on Microsoft Fabric including deployment pipeline (Dev → Prod)
• Forecast comparison report
• Central Power BI apps that bundle all reports by department

Technologies: Power BI, DAX, Power Query (M), SQL, proALPHA, Progress OpenEdge, Microsoft Fabric, Power Automate, Python

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

• Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
• Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
• Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
• Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
• Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
• Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
• Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
• Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.

• Develop data products as part of a Data Mesh / Medallion architecture on Databricks and Azure Synapse.
• Build Power BI dashboards using consolidated data from multiple source systems (sales, service, contracts, inventory data).
• Define KPIs and reporting standards in close collaboration with business stakeholders.

As a freelance IT lecturer and IT coach, I specialize in teaching individuals concepts like Cloud Computing, Business Intelligence, Python Programming and Agile Frameworks. My goal is to simplify complex technical concepts into practical, actionable knowledge. Through my extensive experience as an IT consultant in various sectors and roles I know about the importance of IT training - for companies and employees alike.

Responsibilities: 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 various data sources
• Built monitoring and alerting systems for real-time platform monitoring
• Implemented data versioning and quality checks at every layer
• Designed automated testing and deployment pipelines in GitLab and Bitbucket

Achievements:

• Doubled 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, reducing configuration errors to 0
• Delivered a complete end-to-end data platform at ~10 €/month in infrastructure costs
• Migrated 7 database tables with 0 downstream issues
• Removed 100 % of exposed credentials, eliminating dependency on external vendors
• Delivered 3-team integration in 1 sprint

• Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
• Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
• Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
• Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
• Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
• Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
• Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
• Created and maintained dashboards in Power BI to provide actionable insights.

• Replacement of a Java application and the Oracle DB for loading the internal WerWasWo system using SSIS.
• Development of SSIS packages to load text files into the database (SQL Server)
• Development of a database project for deployment on various servers
• Creation of queries to monitor the loading runs
• Development of a PowerShell script to automate the deployment of the SSDT projects.
• Oracle, SQL Developer, Microsoft SQL Server 2022 on-premises, SQL Server Management Studio v21, Visual Studio 2022, SSIS, SSDT, PowerShell.

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