Data Engineers in Germany
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Meet FRATCH Data Engineers in Germany
Fadi S.
Last position:
AI-Engineer at Recruitment Agency
- Design, development, and productive rollout of an Enterprise HR Copilot based on Microsoft Copilot Studio to automate internal HR processes
- Development of an AI-supported assistant for answering employee inquiries based on a SharePoint knowledge base and internal company HR documentation
- Integration of the Copilot into Microsoft Teams, including welcome logic, user guidance, and role-based routing
- Automation of HR processes and approval workflows with Microsoft Power Automate to reduce manual processing steps
- Development of realistic HR documents and response templates with Azure OpenAI, taking business requirements and consistent company policies into account
- Implementation of prompt strategies, knowledge management, and response optimization to improve response quality and user acceptance
- Close collaboration with business departments to analyze requirements and integrate the AI solution into existing company processes
Technologies:
Microsoft Copilot Studio, Microsoft Teams, Azure OpenAI, SharePoint, Power Automate, Power Platform, Microsoft 365, Prompt Engineering, Workflow Automation
Farid A.
Last position:
SAP Data Migration & Data Management Consultant for SAP GEMINI at Montblanc
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
Henning U.
Last position:
Senior Expert Data Governance, Master Data Quality and Data Migration at E.ON
- 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
Asım Y.
Last position:
Freelance BI/DWH/ETL Specialist at Akbank
- 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
Philipp S.
Last position:
Solution Architect, Software Engineer, UX/UI Designer, Full-Stack Developer, Data Engineer, IT Consultant at Geigenbau-Meisterwerkstatt
- 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
Julian H.
Last position:
IT Project Manager AI Product for Automating Knowledge-Intensive Processes at Leading provider of large-scale catering & food services
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
Stephan F.
Last position:
AI & Data Engineer - Azure Enterprise AI and Knowledge Engineering at IB Schlegel - King
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.
Hervé T.
Last position:
Senior Data Engineer at Schweizerische Post AG
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
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.
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- 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
Alexander B.
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
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
Any-Arlene N.
Last position:
Co-Founder · Data Engineering & Backend at zirikana (Kirundi Bible Web App) – Civic Technology
- 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.
Florian U.
Last position:
Co-Founder & Managing Director at Allbound Solutions GmbH
- 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.
Elnazossadat H.
Last position:
Data Analyst at Siemens Healthineers
- Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
- Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
- Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
- Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
- Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
- Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
- Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
- Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.
Discover over 15,000 top freelancers
Data Engineers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.2 years
Positions per freelancer
11
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
93%
Master's degree or higher
64%
Doctorate
7%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
94%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this role 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.
Discover detailed Data Engineers rate benchmarks:
Explore rate insightsAverage rates for Data Engineers in Germany
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 16 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Engineers 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 (93%)
- Banking and Finance (55%)
- Manufacturing (39%)
- Professional Services (39%)
- Automotive (36%)
- Retail (35%)
- Insurance (33%)
- Energy (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the role
Data pipelines
A data engineer builds the paths that move data from source systems into reliable targets. That includes batch and streaming pipelines, ingestion jobs, transformations, orchestration, and monitoring. Strong work here keeps analytics, reporting, and machine learning teams from fighting broken data.
- Design and build ETL or ELT workflows
- Connect SaaS tools, databases, APIs, and event streams
- Clean, standardize, and model raw data
- Add logging, checks, and alerts for failures
- Document data flows and handover steps
Core skills
A strong Data Engineer knows how to work across data engineering, software engineering, and analytics. The best freelancers understand data modeling, schema design, performance tuning, and how to make pipelines maintainable instead of fragile.
- Python, SQL, and version control
- Spark, dbt, Airflow, and similar workflow tools
- Cloud data stacks such as AWS, Azure, and GCP
- Data warehouses and lakehouse setups
- API integration, testing, and error handling
Typical projects
Companies bring in a data engineer for platform build-outs, migrations, and urgent fixes. Common work includes moving reports to a modern warehouse, building a central source of truth, preparing data for BI dashboards, or setting up streaming data for product and operations teams. In Germany, this often sits close to manufacturing, e-commerce, mobility, finance, and SaaS teams that need dependable data across systems and languages.
When freelance makes sense
A freelancer is a good fit when the workload is project-based, the stack changes quickly, or the team lacks a specific skill for a short period. This is common when a company needs help with a warehouse migration, a new cloud setup, or a pipeline that only one person understands. Remote collaboration works well for most tasks, while on-site time can help when data access, security reviews, or stakeholder workshops are sensitive.
What good looks like
Strong data engineers do more than ship code. They ask where the data comes from, who uses it, what breaks, and how to keep it trustworthy over time.
- Writes readable, tested, production-ready code
- Thinks about data quality, lineage, and recoverability
- Communicates clearly with analysts, engineers, and business owners
- Spots bottlenecks before they become incidents
- Delivers work that others can maintain
Tools and handover
The right Data Engineer can fit into your existing stack without forcing a rewrite. Look for experience with your warehouse, orchestration layer, and deployment process, plus the ability to explain trade-offs in plain language. Good handover includes runbooks, pipeline diagrams, ownership rules, and clear next steps for your team.
Frequently asked questions
What clients ask us most about Data Engineers — answered in short.
A Data Engineer builds and maintains the data flows that companies rely on for reporting, analytics, and operations. They move data from source systems into warehouses or lakehouses, transform it into usable models, and make sure it stays reliable. In practice, that means pipelines, checks, monitoring, and clear handover documentation.
A strong Data Engineer usually works well in SQL and Python and knows how to build reliable pipelines with orchestration and transformation tools. They should also understand cloud data platforms, data modeling, logging, and basic testing. For many projects, good communication matters as much as technical depth.
A Data Engineer prepares the data foundation; analysts and scientists use that foundation to create insight or models. If your problem is broken ingestion, slow pipelines, inconsistent schemas, or messy warehouse structures, you need engineering support. If the data is already clean and the challenge is interpretation, another role may be better.
A freelancer makes sense when you have a clear project, a gap in a specific stack, or a short-term spike in demand. Many companies use a Data Engineer for migrations, pipeline rebuilds, or urgent cleanup work that should not wait for a long hiring process. It is also useful when you need senior help without adding a permanent role yet.
Typical work includes cloud warehouse setup, ETL or ELT design, dbt layer implementation, streaming pipelines, and data quality automation. A Data Engineer is also useful when teams need to connect many systems or replace manual exports with a stable flow. In Germany, this often comes up in manufacturing, finance, e-commerce, and SaaS environments.
Most Data Engineers can work remotely because the job is usually done in code, queries, and planning sessions. On-site time can help during security reviews, access setup, or workshops with data owners and business teams. Many projects work best with a remote core and selected on-site meetings in Germany.
Look for someone who can explain their design choices, not just list tools. A good data engineer shows clean code, sensible naming, testing, monitoring, and a clear plan for failures and recovery. Ask for examples of pipelines they have owned end to end and how they handled bad source data or schema changes.
Freelance Data Engineers should clarify the source systems, target architecture, security rules, and who owns each part of the pipeline. It also helps to confirm whether the client wants fast delivery, long-term maintainability, or both. Clear scope early on avoids rework later, especially in mixed teams across Germany and remote locations.
The average hourly rate for Data Engineers in Germany is 100 €, which corresponds to a daily rate of about 799 € based on an 8-hour working day.
Of the freelancers working as Data Engineers in Germany, 93% hold at least a Bachelor's degree, 64% hold at least a Master's degree, and 7% hold a doctorate.
On average, freelancers working as Data Engineers in Germany have 15 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers working as Data Engineers in Germany are German (99%), English (96%), and French (16%).
The most common industries among freelancers working as Data Engineers in Germany are Information Technology (93%), Banking and Finance (55%), and Manufacturing (39%).
The most common business areas among freelancers working as Data Engineers in Germany are Information Technology (100%), Business Intelligence (91%), and Product Development (62%).
FRATCH Data Engineers main locations
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 
Munich 
Frankfurt Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
