Databricks Experts in Cologne

• Developed a real-time video analytics proof-of-concept for deployment on NVIDIA Jetson edge devices.
• Implemented DeepStream pipelines including object detection, tracking, human pose estimation, face anonymization, and zone intrusion detection.
• Built a Qt/Python demonstration UI interfacing with the AI pipeline via REST APIs.

• 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.

• Delivered ML pipelines for OCR, semantic search, and computer vision
• Integrated Azure AI Agents and GPT workflows for automation and QA
• Deployed cloud-based FastAPI services with scalable architecture
• Created integration docs and advised on LLM production readiness

• 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

• Independently initiated and deployed automated validation workflows using Python, cutting manual processing by 58% and improving efficiency
• Developed a machine learning model for synthetic defect generation, reducing downtime and production costs; deployed locally and via Databricks and Azure AI Factory
• Utilized a small dataset of image data from the production lines and extended this dataset with training on models like cycleGAN and pix2pix
• Built and optimized the Linux-based development environment for training 3D models; maintained reproducibility via GitHub
• Presented technical insights to cross-functional teams (engineers, QA, project managers), ensuring alignment of ML solutions with operational needs

• Implementation of custom reporting solutions for financial KPIs aligned with specific business requirements

• Development of a machine learning application that achieved a 250% performance improvement

• Data Architect "Production-Oriented Quality Assurance" (03/2024–09/2024):

• Design and implementation of an analytics platform to detect quality deviations in manufacturing

• Build of a scalable data lakehouse architecture on Databricks in combination with SAP ERP data via SAP Datasphere

• Close collaboration with the SAP team to harmonize bill of materials and order data

• Visualization of KPIs to support shopfloor management

• Lead Data Engineer "Sales Performance Monitoring" (08/2023–12/2023):

• Design and implementation of a Databricks-based platform for analyzing sales figures and promotion effects

• Integration of SAP SD data via SAP BW/4HANA

• Use of Azure DevOps to orchestrate ETL jobs and deploy workflows

• Technical Project Lead "Cloud Migration & Data Strategy" (02/2023–05/2023):

• Migration of a heterogeneous data warehouse stack to a modern cloud architecture on Azure with Databricks as the central processing platform

• Development of a governance-compliant data architecture to integrate SAP financial data and non-SAP sources

Technologies: Databricks, Delta Lake, Python, SAP Datasphere, Azure DevOps, PowerBI, SQL

• Developed automated ETL pipelines with AWS Glue and Athena to ensure consistent data quality and governance requirements
• Implemented validation, anonymization, and encryption measures for data in compliance with GDPR
• Optimized cloud costs by introducing FinOps practices and increased transparency for business units
• Monitored performance, performed root cause analyses, and ensured adherence to SLAs
• Supported data and solution architects in building scalable data models for ML and analytics scenarios

• Acted as the primary bridge between Legal, Engineering, and Business units to ensure zero compliance violations while maintaining product velocity.

• Led the development of a GDPR-compliant AI aggregator platform, managing a roadmap that balances legal constraints with aggressive feature delivery.

• Scaled the engineering team from 4 to 9 developers, establishing hiring protocols and technical onboarding processes to support rapid product iteration.

• Boosted the development process by introducing structured sprint cycles and backlog refinement, resulting in a 20% reduction in feature delivery time.

• Architected and prototyped agentic AI workflows with n8n and RAG pipelines on Langchain.

• Setting up and enhancing an international subscription management system

• Migrating and decommissioning a Hybris-SAP system and integrating Zuora

• Implementing numerous interfaces to various systems (external and internal)

• Setting up and improving monitoring with Grafana, Prometheus, and Databricks

• Introducing logging with Loki

• Implementing business requirements using jQuery, Thymeleaf, Spring Boot, Java, Go, and Python

• Adjusting and extending the GitLab CI pipelines

• Setting up and extending infrastructure as code with Terraform and ArgoCD on AWS

• Setting up and running E2E and performance tests

• Expanding the Databricks business intelligence solution to provide various KPIs

• Responsibilities: Architecture, DevOps, development, unit tests, coaching employees and apprentices, E2E, smoke, and integration tests, agile methods consulting (Kanban, Scrum)

• Technologies and methods: Java, Go, Kotlin, Node.js, Spring Boot, SOAP/REST, Quarkus, Thymeleaf, Vaadin, Slack, Jira, Confluence, IntelliJ, GitLab, SQS, Kafka, AWS, Kubernetes, Terraform, Maven, Gradle, GitLab CI, Databricks