Azure Data Factory Experts in Germany

• Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
• Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
• Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.

• 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.

• 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.

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

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

• Developed & maintained customer analytical reporting solutions to identify revenue trends, performance drivers, risks & optimization opportunities to ensure data driven decision making across Sales, Finance, Product, Data Engineering & Controlling.
• Defined & analyzed customer trends & performance metrics to identify root causes behind variances, anomalies & emerging risks across business domains to deliver actionable recommendations.
• Developed & owned analytical data models & reporting layers to ensure scalability, performance & analytical robustness to support executive & operational reporting across business domains.
• Planned, tracked & executed projects by ensuring adherence to timelines, data accuracy, consistency, deliverables, reliability & data quality standards through rigorous validation & reconciliation processes.
• Raised the analytical maturity by formalizing analytical workflows, documenting data processes & standard operating procedures (SOPs) & conducting training sessions to drive adoption of self-service analytics & embed a data driven culture across operational and business teams.
• Took ownership of the end-to-end lifecycle roadmap from requirement gathering, collection, transformation, developing robust business logics to data storytelling & stakeholder delivery.
• Converted complexity into structured clarity by translating requirements & business processes into analytical recommendations to ensure alignment between non-technical & technical stakeholders.
• Conducted advanced SQL based analysis of complex business datasets to uncover trends, correlations & performance improvement opportunities.
• Drove process automation & efficiency improvements by leveraging Python, SQL optimization & AI assisted tools to reduce processing time & increase reliability across analytical & operational workflows.
• Standardized KPI definitions & reporting logic to ensure consistency & trust across reporting solutions.
• Developed process monitoring dashboards & analyses to identify inefficiencies, bottlenecks & compliance deviations across end-to-end business processes to derive actionable recommendations for process improvement & automation.

• Responsible for the architecture and development of an AI-native SaaS platform for industrial product portfolio management.
• Designed the modern data platform architecture on Azure for scalable analytics and enterprise data integration.
• Built enterprise data ingestion and transformation pipelines across complex industrial system landscapes.
• Developed graph-based representations of product structures and dependencies for analytical reasoning.
• Designed and implemented an agentic AI framework for AI-supported decision workflows.
• Built scalable analytical microservices and integrated reporting through modern BI technologies.
• Coordinated backend, AI, and frontend development across the MYNR platform stack.

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

• Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
• Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
• Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
• Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
• Designed scalable data foundations suitable for downstream analytics and AI workloads.
• Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.

• Data Engineering

• Business Analyst

• Consulting

• 2025 – present

Data Engineer & Architect

• Azure Data Factory
• Azure Databricks (PySpark)
• PowerBI Service

Lead developer for the further development of the Modern Data Warehouse, as well as Power BI reports and data models based on the ERP systems Amparex and EyeOffice. Focus on topics for the marketing department with data connection via API to Amplitude and Klaviyo with Python

• 2024 – present

Data Engineer & Architect

• SQL Server (T-SQL)
• SSIS (ETL)
• SSAS Tabular
• PowerBI Report Server
• Atlassian Jira & migration to Azure DevOps with GIT (KANBAN)

Development of Power BI reports and data models based on Infor LN ERP data after migration from Baan, including the underlying data structures with Microsoft SQL Server for further development of the Data Warehouse

Consulting for controlling on the specification of business requirements and development of reporting solutions

• 2025 – 06.2025

Data Engineer & Architect

• PowerBI Dataflows
• PowerBI Service

Cloud migration of the Data Warehouse, as well as Power BI reports and data models from Microsoft Dynamics BC 2021 to Microsoft Dynamics BC Cloud, including migration of the underlying data structures from Microsoft SQL Server to Power BI Dataflows

• 2022 – 06.2024 (ongoing support for data engineers)

Data Engineer & Architect

• SQL Server (T-SQL)
• SSIS (ETL) & BIML
• SSAS Tabular
• Azure DevOps with GIT (SCRUM)

Development of analytical models based on Microsoft Dynamics BC 2021 in combination with data from the company's own MariaDB database, including the setup of the underlying data structures with Microsoft SQL Server to build a Data Warehouse. Merging of the ERP systems BC 2021 and DATEV in the Data Warehouse

Consulting for business users on the specification of business requirements and coaching of developers for the development of reporting solutions

• 2022 – 08.2023

Business Analyst & Interim Manager

• PowerBI Service
• Azure Databricks (PySpark)
• Atlassian Jira (SCRUM)

Project management for reporting solution requirements, as well as development of Power BI reports and data models with data from Azure Databricks based on SAP R/3, Google Analytics, webshop and CRM with a business focus on customer, sales and marketing

• 2022 – 05.2022

Power BI Specialist

• PowerBI Service
• Azure DevOps with GIT (SCRUM)

Development of Power BI reports and data models based on data from Exasol with a business focus on assortment management in purchasing

• 2021 – present (ongoing support for existing customers)

Power BI Specialist & Data Engineer

• SQL Server (T-SQL)
• SSIS (ETL) & BIML
• PowerBI Service
• Azure DevOps with GIT (KANBAN)
• Azure Functions (Python)

Development of Power BI reports and data models based on abas ERP data, including the setup of the underlying data structures with Microsoft SQL Server to build a Data Warehouse.

Synchronization of customer data for Pipedrive CRM via Azure Functions with Python.

Consulting for controlling and management on agile project management, the specification of business requirements and development of reporting solutions

• 2021 – present (ongoing support for existing customers)

Data Engineer & Architect

• SQL Server (T-SQL)
• SSIS (ETL) & BIML
• SSAS Tabular
• Azure DevOps with GIT (SCRUM)

Development of analytical models based on Microsoft Dynamics 365 FO, CS & CRM in combination with Microsoft Dynamics AX 2012 R3, including the setup of the underlying data structures with Microsoft SQL Server to build a Data Warehouse

Concept for the migration of the SQL Server-based Data Warehouse to Microsoft Fabric with planned future implementation and temporary use of a hybrid data architecture for the introduction of Power BI Service during the migration phase

Consulting for business users on the specification of business requirements and development of reporting solutions

• Design, development, and maintenance of end-to-end ETL pipelines for scalable and reliable data integration
• Support in data quality checks, testing, and migrations
• Development and maintenance of dbt models for structured, modular, and reusable data transformations
• Use of AI-driven development to improve ETL job creation and code quality.
• Development of CI/CD for automated deployment.

• 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.

• Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
• Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
• Storage of the silver layer on Hadoop and the gold layer in Oracle.
• Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
• Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
• Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
• Versioning changes in GitHub and deployment via the CI/CD portal.
• Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
• Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
• Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
• Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
• Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.