Medallion Architecture Experts in Germany
, matched with vetted freelancers in minutesHire experts who design Bronze, Silver and Gold data layers, implement lakehouse pipelines and improve data quality across analytics environments. FRATCH matches you quickly with precise, vetted and available freelance professionals.
Meet FRATCH Experts in Germany, who have recently used Medallion Architecture
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
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
Umut G.
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.
Alexander Z.
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.
Daryoosh D.
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
Miruna S.
Last position:
Senior Data Analyst / Analytics Engineer
- Develop data products as part of a Data Mesh / Medallion architecture on Databricks and Azure Synapse.
- Build Power BI dashboards using consolidated data from multiple source systems (sales, service, contracts, inventory data).
- Define KPIs and reporting standards in close collaboration with business stakeholders.
Jorge M.
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
Christian S.
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 M.
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 M.
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
Robin S.
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)
Max R.
Last position:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
Leonard H.
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, images and video
- Databricks workspace setup and administration
- Implementation of a medallion architecture to ensure data quality
- Evaluation of potential BI tools (Power BI, AWS Quicksight, Tableau)
- Establishment of MLOps using MLFlow
- Introduction of data governance and data lineage using Unity Catalog
Enrico G.
Last position:
Freelance Software & Data/AI Engineer at Freelance Software & Data/AI Engineer
- Lecturer for the GenAI Track at Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product imports with intelligent column and category classification (4-layer hierarchy), including human-in-the-loop validation
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
1.5 years
Positions per freelancer
12
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Professional Services, Automotive
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
50%
Doctorate
8%
Certifications per freelancer
8
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Medallion Architecture experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (86%)
- Professional Services (64%)
- Automotive (57%)
- Energy (50%)
- Retail (43%)
- Education (36%)
- Banking and Finance (36%)
- Media and Entertainment (36%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data layer pattern
Medallion Architecture is a data engineering pattern that organizes information into progressive quality layers. Raw data enters the Bronze layer, validated and standardized records move into Silver, and business-ready datasets reach Gold. The pattern is also called the medallion pattern and is common in lakehouse environments.
Bronze to Gold
Bronze preserves source data with minimal change, which supports replay, auditing and troubleshooting. Silver applies cleansing, schema control, deduplication and business rules. Gold shapes trusted facts, dimensions, aggregates and domain data products for reporting, machine learning and operational use.
Lakehouse ecosystem
Medallion Architecture often sits across cloud storage, lakehouse engines and orchestration tools. Strong specialists understand how the layers interact with technologies such as Databricks, Delta Lake, Apache Spark, Apache Iceberg, dbt, Airflow and cloud warehouses. They also connect ingestion, cataloging, lineage and access controls.
- Design layer boundaries and data contracts
- Build batch and streaming ingestion pipelines
- Apply schema evolution, quality checks and observability
- Publish curated datasets for BI and machine learning
When expertise matters
Companies bring in freelance specialists when a data lake has become difficult to trust, when pipelines need a clear operating model or when teams are moving toward a lakehouse. Expertise is also useful during Databricks or cloud migrations, domain-level data product work and efforts to reduce duplicate transformations.
In Germany, remote collaboration is common for this work, while regulated industries may require on-site workshops or specific access arrangements. Clear communication in English or German can help teams align on data ownership, definitions and controls.
Strong specialists
A capable professional goes beyond creating three folders or naming tables by layer. They define ownership, freshness expectations, quality thresholds and failure handling for each dataset. They know when raw preservation is essential, when a transformation belongs in Silver and when Gold should serve a particular business decision.
They can explain trade-offs between centralized pipelines and domain ownership. They also make pipelines testable, observable and cost-aware, while documenting lineage and assumptions so another team can operate the system safely.
Project outcomes
A well-designed implementation leaves behind more than working jobs. It provides dependable datasets, repeatable deployments, transparent lineage and clear handoffs between data producers and consumers. Useful deliverables can include a target architecture, ingestion pipelines, transformation models, quality rules, monitoring dashboards and runbooks.
When reviewing a specialist, ask for examples of layer design, incremental processing, late-arriving data and schema changes. Look for practical reasoning about governance, performance and failure recovery, not only familiarity with a particular lakehouse vendor.
Frequently asked questions
Questions about Medallion Architecture? Start with the answers below.
Medallion Architecture organizes data into Bronze, Silver and Gold layers as it moves from source records to trusted business datasets. Companies use it to improve data quality, traceability and reuse across analytics, reporting and machine learning.
The medallion pattern usually preserves raw data in a lakehouse before applying progressively stronger transformations. A traditional warehouse often emphasizes curated schemas earlier, while Medallion Architecture can support raw retention, flexible formats and multiple downstream uses.
A strong Medallion Architecture specialist should understand data modeling, SQL, Python or Scala, orchestration and cloud storage. Experience with Databricks, Delta Lake, Apache Spark, dbt, data quality, cataloging and observability is also valuable.
The right level depends on scope. A simple pipeline may need someone comfortable with layer design and transformations, while a large lakehouse requires deeper judgment around streaming, governance, schema evolution, security and operating costs.
Yes, Medallion Architecture work is often suitable for remote collaboration because design, coding and pipeline reviews happen online. On-site sessions may still help with discovery, regulated data access or workshops involving German-speaking business and compliance teams.
A Medallion Architecture implementation should reflect the data product rather than follow labels mechanically. Small or highly specialized flows may need fewer transformation stages, while complex domains benefit from explicit Bronze, Silver and Gold responsibilities.
Ask how the specialist handles replayable ingestion, duplicate records, late-arriving data, failed jobs and changing schemas. A quality professional can show clear ownership, tests, lineage and monitoring, and can explain why each transformation belongs in its chosen layer.
A Medallion Architecture engagement may produce a target design, data contracts, ingestion jobs, transformation models and quality checks. Strong handover material also includes lineage, deployment instructions, monitoring guidance and runbooks for recovery and maintenance.
The average hourly rate of freelancers in Germany who have used Medallion Architecture in their recent projects is 91 €, which corresponds to a daily rate of about 726 € 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, 50% hold at least a Master's degree, and 8% 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 1.5 years.
The most common languages among freelancers in Germany who have used Medallion Architecture in their recent projects are German (100%), English (100%), and Spanish (14%).
The most common industries among freelancers in Germany who have used Medallion Architecture in their recent projects are Information Technology (86%), Professional Services (64%), and Automotive (57%).
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 (93%).
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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