Data Mesh Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Data Mesh
Jens Henneberg
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilizing an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
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.
Justina Kmiecik
Last position:
Freelance Consultant for Change & Data Transformation at Freelance Fast Data Consulting
Project, Strategic Consulting – building the Data Strategy and Data Governance Policy for the German branch, client (private bank Julius Bär, headquarters Zurich), March 2026 – present
- Design and negotiation of the data strategy with key stakeholders, including obtaining board sign-off (strategic consulting) – in this context, regulatory advice on data regulations in the EU and specifically for Germany. The data strategy includes: Data Lifecycle Management: data capture, data storage, data usage, data retention policy, data quality incident management
- Definition of milestones and technical feasibility for implementing TOM for the data strategy, data quality checks, metrics, and a metadata inventory to ensure the bank’s compliance with DORA, BCBS239, and MaRisk requirements.
Core project data change, client: (ING Bank, Frankfurt am Main), March – December 2025
- Concept development and solution design for new end-to-end processes including technical interfaces
- Definition of synchronization logic and data flows between legacy and target systems (decommissioning of legacy systems)
- Analysis and validation of data models
- Stakeholder communication with product owners, feature engineers, UX designers, and operational teams for decision-making
- Analytics and impact assessments, e.g. to assess downstream effects and regulatory requirements
- Documentation and comments on technical and business requirements to support implementation in agile squads
Project digitalization of a user group, client: (ING Bank, Frankfurt am Main), as Interim Product Owner, Jan 2025 – present
- Co-shaping key decisions on data architecture and process logic in the context of historized data and user login functionality
- Development of business solution concepts for migration to the target system, including system integration and data flows
- Support with analytics and impact analyses, especially regarding the ability to provide information to law enforcement authorities
- Active coordination with stakeholders from different squads to support decision-making and ensure regulatory requirements are met
- Creation of test scenarios for operational teams and backend systems in the area of API management using Postman and Bruno.
Alexander Bromberg
Last position:
Senior Data Engineer at RWE AG
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
Monika Thepale
Last position:
Senior ETL Lead at Takeda GmbH
- Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
- Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
- Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
- Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
- Designed scalable data foundations suitable for downstream analytics and AI workloads.
- Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.
Serge Kalinin
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Benedikt Irsch
Last position:
AI Transformation Coach & Delivery Enablement Lead (Contract) at Digital GmbH
- Drive enterprise AI transformation through alignment of data platforms, AI capabilities, and governance
- Implement scalable Data Mesh architecture to decentralize data ownership and accelerate AI use case development
- Strengthen AI maturity via structural alignment, clarified decision rights, and transparency models
- Coach leaders, product teams, and engineers in AI-enabled decision-making and data-driven product thinking
- Establish governance models for trustworthy AI and measurable business value
- Design flow-based systems (Kanban / Flight Levels) to sustain AI enablement and ensure measurable business integration at scale
- Integrate KPI frameworks, Jira architectures, and BI reporting to enable evidence-based steering of AI initiatives
Robert Wieland
Last position:
Data Architecture Manager at Accenture
- Data Migration Engine / Data Migration from proprietary source systems to SAP/S4 (SAP S/4 HANA Migration cont.)
- Development data authorization Concept
- Conception of system architecture / data architecture / data integration – continuous extensions
- Development conceptional / logical (MDM) data Model - continuous extensions
Marcel Schmidt
Last position:
Technical Lead / Lead Developer at REWE digital
- Due to an organizational restructuring towards product-based teams, the implementation of a Data Mesh in the area of Digital Supplier Management (DSM) was decided.
- A highly available real-time Data Mesh for providing large volumes of data via Kafka and REST to other products.
- New development of a core supplier management software in a (micro-)service-based architecture with a modern tech stack.
- Technologies/Tools:
- Java 21
- Spring Boot
- Angular
- TypeScript
- Go
- MapR
- Kafka
- Docker
- Google Cloud Platform (GCP)
- Kubernetes
- Helm
- GitLab CI
- Maven
- JUnit
- Mockito
- WireMock
- Cypress
- Keycloak
- OAuth 2.0 & OpenID Connect
- Humio
- Nexus
- Artifactory
- SonarQube
- Renovate
- Git
- Postman
- IntelliJ
Maziyar Khorrami
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Björn Gerdau
Last position:
Fullstack Developer at The Coach AI
- Implemented the frontend and backend of a chat application for an AI startup
- Used Go and Dart
- Technologies: gRPC, Flutter, Terraform (IaC)
- CI/CD via GitHub, deployment on Google Cloud Run
Thomas Jarnot
Last position:
Backend Developer, DevOps Engineer at AXA Digital Experience
- Further development, modularization, and technical modernization of the My AXA customer portal using Scrum.
- Connection of new REST APIs to expand the digital product and service portfolio.
- Splitting a monolithic application into clearly defined business responsibilities.
- Continuous refactoring of complex code parts to improve maintainability.
- Supporting other teams with interface design and introducing WireMock for integration tests.
- Standardizing caching and resilience by introducing Spring Cache and Resilience4J.
- Cleaning up request validation according to JSR-170 and developing a simulation process.
- Further development of the code generator to transform OpenAPI contracts into client libraries.
- Development of dashboards and log file analyses using Loki and Grafana Cloud.
- Monitoring and reporting of production and QA systems.
- Tech stack: Java 21, Spring Boot 3, REST, Resilience4J, Docker, OpenAPI, Grafana Cloud, Jenkins, Tekton, OpenShift, AWS, GitLab.
Martin Musiol
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile setup of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
- Extraction of context-relevant content based on user profiles & competency dimensions
- Personalized delivery of learning content to boost sales performance
- Close coordination with sales teams & stakeholders to validate features
- Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
- Integration into existing tools & CRM systems for smooth adoption
- Technologies used: Python, OpenAI, DB tech like PostgreSQL, CI/CD for Airflow DAGs, FastAPI
Oliver Rothland
Last position:
Trainer and Solution Architect for Data Management, Data Mesh, Data Fabric, Observability, Big Data Technologies, Advanced
Supporting national and international companies in building data-driven processes, methods, systems, and applications (Data-Driven Company) in data management and agile requirements engineering.
Identifying essential use cases and (non-functional) requirements to build an architecture on the one hand.
Optimizing clients' internal processes and developing training plans for new technologies and methods.
Combining technical know-how (for example, data analysis in cloud data analytics environments through semantic layers) with key soft skills such as agility, teamwork, creativity, and analytical competence.
Emphasis in data management on efficient use of systems as well as the simple application of technologies for data engineering, data cataloging, virtualization, exploration, and visualization.
Operationalizing both infrastructure as well as data and mathematical analytical models (DevOps, DataOps, MLOps).
Acting as a link between architecture, business departments, development, and operations, taking into account essential core areas such as data governance, data security, and data quality.
Alexander Steiner
Last position:
Vice President at Douglas – Beauty & Retail
- Organizations lead up to 700+ people in Düsseldorf and various European countries
- Building and transforming organizations
- Managing strategic IT projects and operational budget
- Organizations control (120m)
- Change management is a key factor to success
Discover over 15,000 top freelancers
Statistics of experts using Data Mesh
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
4.7 years
Positions per freelancer
12
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
57%
Doctorate
14%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
94%
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 Data Mesh
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
Data mesh basics
Data mesh is a way to organize data work around business domains instead of one central data team. It treats data as a product, with clear ownership, quality rules, and discoverable access. Companies use it to scale analytics and reporting without creating a bottleneck.
What experts deliver
- Domain data products with clear ownership
- Data contracts and schema governance
- Self-serve pipelines and shared standards
- Catalog, lineage, and access patterns
- Operating models for cross-team data use
Tools and stack
Strong specialists work across warehouse, lakehouse, orchestration, catalog, and governance layers. They often align data mesh with tools for metadata, transformation, quality checks, and secure access control. The right setup depends on the existing stack, not a fixed framework.
When companies bring help
Teams usually look for freelance support when data ownership is unclear, platform work is moving slower than domain delivery, or governance has become too central. Data mesh also comes up during cloud migration, analytics modernization, and mergers where many data sources must work together.
What strong specialists do
Good professionals can translate business domains into practical data products. They define interfaces, make data reusable, and keep governance light enough for delivery. They also know where data mesh fits and where a simpler central model is still the better choice.
Germany context
In Germany, data mesh work often sits between product, analytics, and platform teams in larger industrial, financial, and retail settings. Remote collaboration is common, but on-site workshops can help with ownership, domain mapping, and governance alignment. Clear English is often enough for delivery, while German helps in stakeholder-facing work.
Frequently asked questions
Key details about Data Mesh, drawn from the questions we get asked most.
Data Mesh is used to scale data delivery across many teams without forcing everything through one central group. It helps companies publish domain-owned data products, define access rules, and keep analytics easier to trust and reuse. It is common in organizations with many source systems and shared reporting needs.
Data Mesh distributes ownership across domains, while a central warehouse model keeps more control in one place. A warehouse can be simpler at first, but it may become a bottleneck as demand grows. Data mesh works best when multiple teams need to own and ship data independently.
No. Data Mesh is an operating model for ownership, product thinking, and governance, while data fabric is more about integrated data access and automation across systems. They can coexist, but they solve different problems and should not be treated as synonyms.
A strong Data Mesh freelancer usually understands data modeling, pipelines, governance, cataloging, and cloud data tooling. Skills in data contracts, observability, and team operating models matter as much as technical setup. Business-domain thinking is also important because the model depends on clear ownership.
Data Mesh fits best when a company has several domains, repeated data handoffs, and enough complexity that a central team cannot keep up. For small or early-stage setups, it can add overhead. A good specialist will assess the current pain points before recommending it.
Yes. Data Mesh often sits on top of an existing warehouse or lakehouse rather than replacing it. The main change is how ownership, publishing, access, and quality are organized across domains.
For Data Mesh, remote work is often fine for delivery, design, and documentation. On-site workshops can help when teams need to agree on domain boundaries, governance, or stakeholder roles. In Germany, many projects use a mix of both depending on the phase.
Look for someone who can explain trade-offs clearly and connect the model to real delivery, not just theory. A strong Data Mesh specialist will show examples of data products, ownership patterns, governance decisions, and how teams actually adopted the approach. If the plan sounds abstract, it is usually a warning sign.
The average hourly rate of freelancers in Germany who have used Data Mesh in their recent projects is 106 €, which corresponds to a daily rate of about 852 € based on an 8-hour working day.
Of the freelancers in Germany who have used Data Mesh in their recent projects, 100% hold at least a Bachelor's degree, 57% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Germany who have used Data Mesh in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 4.7 years.
The most common languages among freelancers in Germany who have used Data Mesh in their recent projects are German (100%), English (94%), and French (19%).
The most common industries among freelancers in Germany who have used Data Mesh in their recent projects are Information Technology (88%), Banking and Finance (63%), and Professional Services (56%).
The most common business areas among freelancers in Germany who have used Data Mesh in their recent projects are Information Technology (100%), Product Development (88%), and Business Intelligence (81%).
Main locations of FRATCH Experts, who have recently used Data Mesh
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
