Medallion Architecture Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Medallion Architecture
Fadi Shoaa
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Umut Gülac
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Daryoosh Dehestani
Last position:
FP&A Data & AI Architect at Epta Group
Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.
Financial Data Integrity & ERP Governance
- Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
- Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
- Validated SAP reports, establishing baseline data quality standards for Finance team consumption
- Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs
Finance Reporting Transformation
- Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
- Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
- Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
- Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models
Power BI & Analytics Enablement
- Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
- Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
- Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team
Transformation Infrastructure & Collaboration
- Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
- Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
- Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization
Outcomes
- GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
- Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
- Power BI transformation roadmap presented and approved by Finance leadership
- Jira-based project governance live; Finance transformation now tracked with full sprint visibility
Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python
Alexander Zhirov
Last position:
Senior Data Solutions Engineer at VMware Inc.
- 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.
Jorge Machado
Last position:
Technical Lead / Fractional CTO at Würth GmbH
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
Enrico Goerlitz
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Vili Dhamo
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, 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
Christian Schulz
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Kevin Meinon
Last position:
Backend & Infrastructure Engineer at Mileo Systems GmbH
- Engineered production-ready Azure environments using Terraform, ensuring consistent infrastructure parity across VNets and Resource Groups
- Implemented Microsoft Fabric tenant and workspace architecture for multi-stage Medallion data processing pipelines
- Designed secure data pathways using Managed Private Endpoints for isolated Azure Storage access
- Managed Service Principals and authentication tokens for secure REST API integrations
Jorge Machado
Last position:
Data Architect at Deutsche Bahn
- Design and provide best practices on data modeling for dbt, including changing dimensions, late arriving data handling, and testing
- Design the ingestion flow from other systems into S3 and Redshift
- Design and implement new partitions for Dagster and incremental loading with dbt
- Map business requirements to technical architectures
- Instruct junior team members
Leonard Hußke
Last position:
Freelance Software Engineer & Cloud Architect at Leonard Hußke - IT Solutions
- Evaluation of potential providers (Snowflake vs Databricks) and design of the analytics data platform using Databricks
- Data storage and ingestion layer with Amazon S3
- Creation of ETL processes and data transformations with AWS Glue and Databricks Notebooks
- Orchestration with AWS Glue Workflow, Databricks Workflow and Databricks DLT
- Processing of unstructured data including text, image and video
- Databricks workspace setup and administration
- Setting up a medallion architecture to ensure data quality
- Evaluation of possible BI tools (Power BI, AWS QuickSight, Tableau)
- Establishing MLOps using MLflow
- Introducing data governance and data lineage using Unity Catalog
Robin Steinkühler
Last position:
Consultant, Data Science & Engineering at valantic Digital Finance GmbH
- Bridged business and engineering for enterprise finance clients, designing data products and cloud pipelines in Python, SQL Server, SAP Datasphere, and Tagetik
- Conceived, built, and containerised a Python/FastAPI universal connector that syncs SAP S/4HANA and other SQL/NoSQL sources to Tagetik, deployed on Google Cloud Run and Microsoft Azure, cutting a critical 90-minute data load to approximately 80 seconds (65× faster)
- Architected a medallion-layer SQL Server warehouse ingesting approximately 500 GB/day from 11 ERP instances, automating daily refreshes (full load under 6 minutes) and freeing 20–30 finance staff from days of manual data consolidation
- Led cross-functional workshops to design enterprise EPM target architecture for a leading Southeast-Asian telecom (CAPEX, OPEX, revenue), translating requirements into data-model specifications and integration blueprints now being built by the client’s implementation team
- Delivered selected projects including a consolidated data & reporting warehouse for a global manufacturer (10 k+ employees), NFI reporting for an international management & technology consultancy, and CAPEX/OPEX planning for a Southeast-Asian telecom (20 k+ employees)
Discover over 15,000 top freelancers
Statistics of experts using Medallion Architecture
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.2 years
Positions per freelancer
12
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Professional Services, Energy
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
40%
Doctorate
10%
Certifications per freelancer
8
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Medallion Architecture
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Layered data design
Medallion Architecture is a way to structure data pipelines in clear layers. Raw data lands in Bronze, cleaned and standardized data moves to Silver, and trusted business-ready data is published in Gold. Teams use it to make lakehouse data easier to manage, test, and reuse.
Where it fits
- Streaming and batch ingestion
- Data cleansing and deduplication
- Business reporting and analytics datasets
- Feature prep for data science work
- Curated data products for shared use
It is common in lakehouse stacks built on Databricks, Delta Lake, Apache Spark, and cloud storage services. Strong specialists know how to keep each layer simple, traceable, and easy to evolve.
Why companies bring help
Companies often need freelance experts when pipelines have grown messy or when a new lakehouse design is being introduced. Medallion Architecture helps separate raw inputs from validated outputs, but that only works if rules for quality, lineage, and ownership are defined well.
What strong specialists do
A good specialist thinks in data contracts, schema evolution, and repeatable transformations. They design jobs so Bronze stays close to source data, Silver is trusted and consistent, and Gold is ready for dashboards or downstream teams.
Skills around it
Medallion work usually sits close to Spark SQL, Python, Delta Lake, dbt, and cloud data services. In Germany, companies often want experts who can work with local product teams in English and still align with internal data governance and reporting needs.
Signs you need it
- Multiple teams transform the same data in different ways
- Quality checks happen too late in the pipeline
- Reporting data and raw source data are mixed together
- New users cannot tell which dataset is trusted
- Existing lakehouse layers are hard to maintain
This architecture is a strong fit when data volume, reuse, and governance matter more than one-off pipelines. The best professionals keep the design practical, documented, and easy for other specialists to extend.
Frequently asked questions
Questions about Medallion Architecture? Start with the answers below.
Medallion Architecture is used to organize data into clear stages so raw inputs, cleaned records, and trusted outputs stay separate. That makes lakehouse pipelines easier to test, govern, and reuse. It is especially useful when many teams depend on the same data.
Yes. The Bronze, Silver, and Gold layer model is the most common way people describe it, and many search for the same idea under those names. Bronze keeps source data close to raw, Silver adds cleaning and standardization, and Gold publishes business-ready datasets.
Medallion Architecture is usually built in a lakehouse or data lake environment, while a classic warehouse design often centers on modeled tables inside a warehouse. The medallion approach gives more flexibility for raw and semi-structured data. It also helps teams keep lineage clearer across transformation stages.
A strong Medallion Architecture specialist usually knows Spark, SQL, Python, Delta Lake, and data modeling basics. Experience with dbt, orchestration, data quality checks, and cloud storage is also valuable. The best experts can explain trade-offs, not just write transformations.
You do not need a fully finished design, but you should know the main sources, target consumers, and pain points. A Medallion Architecture expert can help define layer boundaries, transformation rules, and quality controls from there. Clear access to sample data and current pipeline logic speeds up the start.
Yes, most Medallion Architecture work can be done remotely, including design reviews, pipeline implementation, and documentation. For German teams, remote collaboration often works well when data access, security review, and meeting language are agreed early. On-site time is mainly useful for workshops or stakeholder alignment.
Look for clear examples of how the person handled Bronze, Silver, and Gold boundaries, not just tool names. A good Medallion Architecture professional can explain lineage, schema evolution, and data quality decisions in plain words. They should also show how they prevented duplication and made the pipeline easier to operate.
Medallion Architecture helps prepare trustworthy datasets for dashboards, reporting, and feature engineering without mixing raw data with curated outputs. That structure makes it easier to reuse the same foundation for analytics and downstream machine learning work. It also reduces confusion about which dataset is the source of truth.
The average hourly rate of freelancers in Germany who have used Medallion Architecture in their recent projects is 88 €, which corresponds to a daily rate of about 706 € based on an 8-hour working day.
Of the freelancers in Germany who have used Medallion Architecture in their recent projects, 100% hold at least a Bachelor's degree, 40% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Germany who have used Medallion Architecture in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.2 years.
The most common languages among freelancers in Germany who have used Medallion Architecture in their recent projects are German (100%), English (100%), and Arabic (8%).
The most common industries among freelancers in Germany who have used Medallion Architecture in their recent projects are Information Technology (100%), Professional Services (67%), and Energy (58%).
The most common business areas among freelancers in Germany who have used Medallion Architecture in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (92%).
Main locations of FRATCH Experts, who have recently used Medallion Architecture
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
