Data Engineers in Germany

• Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility

• Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture

• Pipeline orchestration with Azure Data Factory

• Improved and automated the development and deployment process by integrating Git and GitHub Actions

• Technical leadership of the Data Engineering team (3 employees)

• Led workshops to optimize and stabilize the data platform and development process

• Collected and prioritized new requests and maintained the product backlog

• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural

• Taught ML, BI, and Big Data to Master's students.

Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM

• 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.

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

Independently built a data platform with cross-system process monitoring.

• Designed the Bronze/Silver/Gold architecture and Python-based pipelines for diverse operational sources.
• Cleaned, harmonized, and validated data; processed and stored it in Parquet format.
• Tracked data history and built business-focused fact and dimension models for traceable reporting.
• Automated data delivery with Python and Power Automate (desktop and cloud); used SharePoint for archiving and data access.
• Developed the Power BI solution from data preparation through to visualizing process statuses, work progress, and deviations.

Technologies: Python, SQL, Parquet, Power BI, Power Query, Power Automate, SharePoint, Copilot

• 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

• Setting architectural standards for Informatica Power Center development teams
• Maintenance-performance-health for DWH (Exadata) environments
• Qlik Sense / BO reports architectural standards
• Implementing best practices in ETL tasks
• Documentation ETL standards on Confluence
• Dynamic Data Masking rules for GDPR/KVKK regulations by using Informatica DDM tool
• DWH Data model Power Designer
• Upgrade Qlik Sense versions
• Merge all Informatica server's repository databases on one Oracle DB

• Designed and developed the central company platform LINK: tasks, CMS, shop, academy, image database, fleet records, and AI assistant in a shared data model on PostgreSQL.
• Supported the transformation of established standalone solutions into a cloud architecture; analyzed the existing data landscape and migrated it step by step.
• Integrated internal and external data sources via APIs and webhooks: Microsoft 365, shop with accounting integration, and manufacturer data.
• Implemented an AI assistant for knowledge access and document analysis, including an approval process before sending.
• Migrated and rebuilt a 49-page website with SEO in mind; launched data-driven tools (Drone Finder, Permit Finder) and lead forms.
• Served as the link between management, operations, and IT: analyzed processes on-site, translated requirements into technical solutions, and trained a team with little digital experience using real work.

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.

• Automated analysis of test script repositories using Python
• Set up a SQLite database for signal and value tracking
• Identified critical inconsistencies through ad hoc SQL queries
• Technologies: Python, SQLite, Excel/ODBC

• Lakehouse build: Designed and built the member & eligibility domain of an Azure Databricks healthcare lakehouse, ingesting SQL Server, file and REST/SFTP sources (JSON, CSV, XML, Parquet) via Azure Data Factory into ADLS Gen2.
• Medallion pipelines: Transformed data through bronze, silver and gold Delta Lake layers with PySpark and Spark SQL; used Delta Live Tables / Lakeflow with built-in expectations for declarative, quality-checked pipelines.
• Platform modernisation: Delivered a SQL Server-to-cloud platform: ADF → ADLS Gen2 → Databricks → dbt Core models → Snowflake → Power BI, replacing legacy reporting with governed analytics.
• Robust ETL: Built metadata-driven, idempotent full, incremental and watermark-driven loads with schema evolution and failure-state handling, improving reliability and re-runnability.
• Data modelling: Designed Kimball star schemas with SCD Type 1 & 2 dimensions; tuned slow queries and pipelines using execution plans.
• Governance & security: Applied Unity Catalog RBAC, lineage and audit logging, with Azure Key Vault secrets and managed identities, to meet HIPAA and GDPR requirements.
• Leadership & mentoring: Led a 6-person team (2 data engineers, 2 analysts, BI developer, QA); mentored engineers, set delivery standards and coordinated with international stakeholders.
• Delivery & AI-assisted work: Ran CI/CD and work tracking in Azure DevOps; built Power BI semantic models with DAX and RLS; used Genie Code, Claude and Copilot to accelerate PySpark/SQL development, DAX and documentation.

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

Designed and developed an Azure-based AI and data platform transforming distributed project documents into searchable, structured knowledge for an enterprise AI assistant. Combined incremental document processing, hybrid keyword/vector retrieval, knowledge graph extraction, and conversational AI for Microsoft Teams and Microsoft 365 Copilot. Implemented Python document-processing pipelines for PDF, Word, Outlook and text files; Azure Cosmos DB lifecycle management; Azure AI Search; Azure AI Foundry and Azure OpenAI; identity-aware retrieval with Microsoft Entra ID; project authorization; a TypeScript Microsoft Agents SDK agent; Pulumi infrastructure; resilient scheduling, retry and diagnostic tooling. Technology stack: Python, TypeScript, FastAPI, Pydantic, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Cosmos DB, Blob/Table Storage, Container Apps/Jobs, Service Bus, Key Vault, Entra ID, Docker, Pulumi, pytest, Microsoft Teams and Microsoft 365 Copilot.