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:
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
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
Marco S.
Last position:
Business Analyst, Data Warehouse Developer at NRW.Bank
Business analysis for risk controlling.
Development of a data warehouse based on MS SQL Server with data from the FIS Cross-Asset Trading and Risk Platform (formerly Front Arena).
Implementation of ETL and transformation logic with T-SQL and Python (Jinja2 template engine).
Modeling and automation of data structures using Data Vault.
Development of reporting and analysis reports with Microsoft Power BI, including user training and onboarding.
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).
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.
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
Elija L.
Last position:
Fujitsu Germany
- Supporting and helping shape the setup 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
- Designing and implementing data modeling, ETL processes, reporting, and data quality management
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
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
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.
Rainer S.
Last position:
Microsoft DAT207x Certificate at edX
- Earned the Microsoft DAT207x certificate on edX.
- Completion date: 21.01.2020.
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
Octavian G.
Last position:
Oracle DWH Analyst at Infomotion Gmbh
Oracle PL/SQL, Oracle SQL, ITIL
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
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
91%
Master's degree or higher
62%
Doctorate
6%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
93%
Based on our profile pool as of 10 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this role 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 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 10 Sep 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 (92%)
- Banking and Finance (55%)
- Manufacturing (41%)
- Professional Services (39%)
- Retail (34%)
- Automotive (33%)
- Insurance (33%)
- 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 99 €, which corresponds to a daily rate of about 794 € based on an 8-hour working day.
Of the freelancers working as Data Engineer, 91% hold at least a Bachelor's degree, 62% 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 (97%), English (95%), and French (18%).
The most common industries among freelancers working as Data Engineer are Information Technology (92%), Banking and Finance (55%), and Manufacturing (41%).
The most common business areas among freelancers working as Data Engineer are Information Technology (100%), Business Intelligence (93%), and Product Development (59%).
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
