Data Engineers in Germany

The consultant's responsibilities/actions are:

• Consulting on the migration of material master & PP Master data
• Prepare Article List template for Cutover Phase
• Implementation of data cleansing measures using individual and bulk changes
• SAP all mandatory fields data extraction regarding Business needed
• SAP migration, Cutover, Testing, BAT, UAT
• Align with the multiple Stakeholders regarding Data from Legacy and SAP System
• Deployed SAP MM best practice (guided configurations, active methodologies and road map for project initiation)

SAP ERP | SAP GEMINI | SAP Fields Coordination | Documentation | SAP IDoc | SAP Integration | SAP End-to-End Process | SAP MM (Material Management) | SAP Functional Consultant | SAP Gap analysis | SAP MDM (Master Data Management) | Data Migration & Management

• Planning and implementation of a migration strategy for master and transaction data for the continuous loading of a cloud-independent database
• Creation and pilot implementation of a company-wide Business Data Model for customers, suppliers, contracts, products, prices, consumption, invoices, and dunning
• Concept and consulting for a Data Governance Framework incl. definition of committees, roles, processes, and metadata model
• Operationalization of the Data Governance Framework with definition of data standards
• Sub-project management in two pilot projects (PoCs) for Data Governance systems (ErwinDIS and Atlan)
• Training and coaching the data team in migration, data quality, and data modeling

• A digital system made up of special software and hardware components. The overall system replaces the traditional process with job slips and handwritten notes and enables more efficient order intake. Orders and work steps for the violin-making company’s projects can now be recorded, processed and logged in real time directly on the workshop’s touchscreen PC, by mobile phone or on the desktop. This gives customers a more transparent view of the work on their instruments and allows them to track the status and progress of their instrument through their customer account.

Tech stack: next.js, React, Flutter, Dart, Raspberry, Linux, Directus

Project: Design and implementation of an AI product for four business use cases

Project management for an AI project at a leading provider of large-scale catering and food services, where a production-ready AI product for four use cases was implemented together with an external development partner: automated briefings based on CRM and document data, voice-based capture and structuring of reports, detection and consolidation of duplicates in master data, and data-based market analyses. A key focus was a data-protection-compliant architecture that passed the internal IT security review and enabled production use.

• Translation of business requirements into clearly defined AI use cases with a focused product scope and clear value proposition
• Selection and evaluation of models and architecture options for text extraction, speech-to-text, and context enrichment from business systems, including LLM integration, function calling, and retrieval
• Development and implementation of an architecture with European hosting, data minimization, and masking of personal data as a prerequisite for approval
• Management of interfaces between business departments, IT, IT security, and the external development partner under restrictive data access conditions
• Coordination with the CIO and executive management levels on data access, risk assessment, and approval decisions
• Preparation for the transition to production use

Tools: Fabric, AWS, dbt, Power BI, SQL, DWH, R, Python

• Supported customers in implementing an architecture design for extracting and preparing data
• Planned the design and implementation of the BI and DWH platform
• Ensured the scalability and performance of the data platform

• 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.

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

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

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.

• Built a Python pipeline that converts lectionary web content into structured daily JSON, applying liturgical-calendar rules for accurate weekday and Sunday coverage.
• Shipped a read-only FastAPI REST API with shared Pydantic models and delivered a Kirundi-first web client for browser and mobile use.
• Owned the data layer and backend architecture, collaborating closely on system architecture and interfaces while automating refreshes with GitHub Actions and validating the ETL with pytest.
• Impact: Created a reliable, API-driven source of truth for daily Bible readings in Kirundi, enabling consistent access to previously unstructured content.

• Implemented customer-specific data, GTM, web, and automation systems from requirements gathering through controlled deployment.
• Built multi-tenant research and delivery platforms with TypeScript/Node.js, React, APIs, LLM integration, and automated checks.
• Connected HubSpot, Notion, Clay, Smartlead, n8n, and Make with traceable handoffs, deduplication, error paths, and approvals.

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

• Development of ETL pipelines with Talend (7/ Enterprise)
• Development/adaptation of database schema/database functions (PostgreSQL)
• Development of GIT CI pipelines

Technologies: Talend Enterprise, Git, PostgreSQL, Dbeaver, SQL, PL-SQL, Liquibase