Data Engineer
in minutes from 15,000 CVs with the power of AIGet support for ETL and ELT pipelines, data warehousing, streaming data, and cloud platforms like Snowflake, BigQuery, Databricks, and Azure Data Factory. FRATCH helps you match fast and precisely with vetted, available freelancers.
Meet FRATCH Data Engineer
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
Octavian G.
Last position:
Oracle DWH Analyst at Infomotion Gmbh
Oracle PL/SQL, Oracle SQL, ITIL
Marco S.
Last position:
MS Power BI Developer at Bayern Facility Management GmbH
Power BI development and administration, training, consulting, data modeling, DAX development
MS Fabric, MS Power BI, DAX
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
Karl F.
Last position:
ONECEPT Website at ONECEPT GmbH
Development and implementation of the website onecept.at using React/Next.js. Focus on responsive web design, high-performance component structure, basic SEO optimization, and technical deployment.
Technologies: React, Next.js, TypeScript / JavaScript
Paulo M.
Last position:
Power BI / Fabric & SAP BI Consultant at Financial Bank
- Developed Power BI semantic models, DAX and RLS, improving reporting performance and security.
- Designed Fabric Lakehouse with Medallion Architecture (Bronze/Silver/Gold), accelerating data processing and improving maintainability and scalability.
- Built Fabric Data Factory pipelines and PySpark transformations, reducing processing time by 40%.
- Delivered automated Power BI KPI dashboards, accelerating reporting and decision-making.
- Supported SAP BO 4.3 → 2025 migration across 2,000+ Webl reports and 34 universes, reducing testing effort by 35% and improving migration quality and reliability.
- Optimized UNX universes, reducing query execution time by 40%.
- Optimized SAP BO reporting solutions, improving reliability and reducing manual effort.
Environment: Power BI, Microsoft Fabric (Lakehouse, Warehouse, Pipelines, PySpark), Tabular Editor, DAX Studio, SAP BI (BO 4.3/2025, Webl, UNX Universes), 360Suite, InfoBurst, GitHub, JIRA.
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
Elija L.
Last position:
Fujitsu Deutschland
- Supporting and helping shape the development and further development of a company-wide HUB system in the Fujitsu environment for integrating a wide range of data sources and applications in an international context
- Design and implementation of data modeling, ETL processes, reporting, and data quality management
Kalin S.
Last position:
Sr Data Engineer / Architect at Samsung Logistics
- Architected and implemented a structured three-layer enterprise data warehouse model in Azure, establishing a robust and scalable data environment.
- Migrated legacy stored procedures to streamlined Azure Data Factory (ADF) pipelines, enhancing data processing efficiency.
- Introduced comprehensive Git-based source control, ensuring rigorous version management and collaborative development practices.
- Established automated data quality frameworks with proactive monitoring and alerting, significantly improving data integrity and reliability.
- Spearheaded the design of an enterprise data model, enabling a self-service BI environment that empowered business teams with advanced analytics capabilities.
- Developed and delivered insightful dashboards and reports in Power BI, transforming raw data into actionable business insights.
- Technologies: Azure Data Factory (ADF), Azure, MS-SQL, DataVault 2.0, Git, Power BI, data quality automation, Agile/Scrum, data modeling, self-service BI, stakeholder 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
Stefania D.
Last position:
Data Engineer at Storebox
Tech: AWS (Glue, Lambda, Redshift), Airflow, PostgreSQL, Python, PySpark, Metabase, Power BI
Delivered: Analytics-Ready Data Models • Legacy SQL to Cloud ETL Migration • Dynamic Pricing Engine
- Owned and evolved the company data warehouse end-to-end — from ingestion to transformation to analytics-ready dimensional data models on AWS Redshift.
- Collaborated with Analysts, Data Scientists, and business stakeholders to deliver scalable dimensional data models that enable self-serve analytics and streamline dashboarding in Metabase and Power BI.
- Architected end-to-end ETL/ELT pipelines on AWS (Glue, Lambda, Redshift) using Python and PySpark, orchestrated with Apache Airflow (MWAA) for reliability and observability.
- Defined and enforced data quality standards and governance practices across pipelines and the core data layer.
- Led migration of legacy SQL infrastructure into scalable AWS Glue pipelines with distributed PySpark processing, eliminating bottlenecks and reducing downtime.
- Developed a dynamic pricing engine applying automated promotional discounts based on occupancy rates, competitor pricing, and location performance tiers.
- Designed schema mappings to ingest MongoDB data into structured relational systems (Redshift/PostgreSQL).
Rainer S.
Last position:
Microsoft DAT207x Certificate at edX
- Earned the Microsoft DAT207x certificate on edX.
- Completion date: 21.01.2020.
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
Hossein A.
Last position:
Datawarehouse Consultant at LENZING AG
- Consulting on the enterprise data-warehouse and reporting practice at one of Austria's largest industrial groups.
- Designing and standardizing Power BI dashboards and data-visualization governance for company-wide enterprise reporting.
- Developing a WCAG-compliant, colorblind-safe visualization standard (Okabe-Ito palette) to harmonize dashboards across the organization.
Karl E.
Last position:
incl. CI/CD, automation at AALS Software AG
- Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
- Gained extensive DevOps and CI/CD experience
- Created ETL/ELT pipelines with Apache tools and Pentaho
- Led projects in municipal software, financial services, and big data with Kafka
- Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
- Built and managed a TypeDB knowledge database
- Worked with OpenStack, Kubernetes, and Podman
Discover over 15,000 top freelancers
Data Engineer statistics
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.3 years
Positions per freelancer
12
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
92%
Master's degree or higher
64%
Doctorate
6%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
94%
Based on our profile pool as of 21 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this role 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 for Data Engineer
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 21 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Engineer 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 (89%)
- Banking and Finance (54%)
- Manufacturing (40%)
- Professional Services (38%)
- Retail (37%)
- Automotive (35%)
- Insurance (31%)
- Energy (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the role
What they build
A Data Engineer turns raw data into reliable flows that teams can use. They design pipelines, connect source systems, clean and shape data, and deliver stable datasets for analytics, reporting, and machine learning.
- ETL and ELT pipelines
- Data warehouses and lakehouses
- Streaming and batch data jobs
- Data models, schemas, and orchestration
- Monitoring, logging, and failure handling
Core skills
Strong Data Engineers understand SQL, Python, data modeling, and cloud infrastructure. They know how to move data safely between business systems, APIs, files, and platforms without breaking quality or lineage.
Common tools include Airflow, dbt, Spark, Kafka, Snowflake, BigQuery, Databricks, Redshift, and Azure Data Factory. Good freelancers also know how to write maintainable code, document their work, and hand over clean setups to internal teams.
When to bring one in
Companies hire a freelance Data Engineer when data work is urgent, specialized, or tied to a short project. That often includes platform migrations, pipeline rebuilds, reporting fixes, GDPR-sensitive data flows, or preparing a stack for analytics and AI use.
This role is also a good fit when the team lacks deep platform knowledge or needs extra capacity without adding a permanent headcount. Freelancers work well on new builds, legacy cleanup, and temporary support for product launches or data incidents.
What strong people do
The best Data Engineers do more than move records from A to B. They think about data quality, cost, access control, and future maintenance from the start.
- Build pipelines that are reliable and easy to extend
- Catch bad source data before it reaches reporting layers
- Keep ownership clear across engineering and analytics teams
- Document logic, dependencies, and deployment steps
- Fix bottlenecks in slow or fragile data flows
Adjacent roles
Job titles can vary. Companies may look for a data engineer, analytics engineer, ETL developer, data platform engineer, or cloud data engineer when they need this kind of work. The exact title matters less than the delivery scope.
An Analytics Engineer usually works closer to the transformation and semantic layer for reporting. A Data Engineer sits deeper in the stack, closer to sources, pipelines, storage, orchestration, and system reliability.
How to brief the work
A clear brief helps the freelancer start quickly. Share your source systems, target tools, data volume, refresh needs, quality rules, and what “done” means for the business.
Mention whether the work is greenfield or existing stack recovery, who owns the cloud account, and which teams need to review the output. The stronger the handover, the faster the delivery.
Frequently asked questions
Not sure where to start with Data Engineer? These answers cover the essentials.
A Data Engineer builds and maintains the data flows that move information from source systems into usable storage and reporting layers. That can include ingestion, transformations, orchestration, testing, monitoring, and fixes for broken pipelines. The goal is clean, dependable data that teams can trust.
A strong Data Engineer should be solid in SQL, Python, and data modeling, and comfortable with cloud data platforms. They should also understand pipeline design, data quality checks, and basic DevOps habits such as version control and deployment discipline. For more complex work, knowledge of Spark, Kafka, Airflow, or dbt is often important.
A Data Engineer usually works closer to source systems, ingestion, orchestration, and the reliability of the overall data stack. An Analytics Engineer spends more time shaping data for reporting and the semantic layer used by analysts. In smaller teams, one freelancer may cover both, but the depth of work is different.
A freelancer makes sense when the work is project-based, urgent, or tied to a specific platform change. That is common for migrations, new data products, legacy cleanup, or short-term gap filling after someone leaves. A freelance Data Engineer can start faster and focus on delivery without long ramp-up needs.
Typical deliverables include pipeline code, transformation logic, data models, documentation, and monitoring setup. Depending on the project, the Data Engineer may also deliver migration plans, test cases, access patterns, or handover notes for internal teams. The more specific the scope, the easier it is to judge success.
Yes, many Data Engineers work well remotely because most tasks are handled in code, cloud tools, and shared documentation. On-site time can help when the work depends on sensitive systems, stakeholders with unclear requirements, or a team that needs close collaboration at the start. The best setup depends on how your data environment is run.
Ask for examples of pipelines, platforms, and production problems they have handled, not just tool names. A good data engineer can explain trade-offs, show how they handle failure, and describe how they protect data quality and maintainability. Clear communication is as important as technical depth.
Freelancers should ask about the source systems, target architecture, business goals, and ownership of access and deployments. They also need to know who approves changes, how data quality is checked, and whether the stack is new or already fragile. That context helps them plan the work and avoid surprises.
The average hourly rate for Data Engineer is 98 €, which corresponds to a daily rate of about 782 € based on an 8-hour working day.
Of the freelancers working as Data Engineer, 92% hold at least a Bachelor's degree, 64% hold at least a Master's degree, and 6% hold a doctorate.
On average, freelancers working as Data Engineer have 16 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers working as Data Engineer are German (98%), English (95%), and French (20%).
The most common industries among freelancers working as Data Engineer are Information Technology (89%), Banking and Finance (54%), and Manufacturing (40%).
The most common business areas among freelancers working as Data Engineer are Information Technology (100%), Business Intelligence (93%), and Product Development (58%).
FRATCH Data Engineer main locations
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Munich 
Frankfurt In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Zurich Geneva Basel Bern Countries:
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Philipp Thomaschewski
FRATCH CEO
