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 Shoaa
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
Marco Skulschus
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 (template engine Jinja2).
Modeling and automation of data structures with Data Vault.
Building reporting and analysis reports with Microsoft Power BI, including training and onboarding of users.
Karl Forstner
Last position:
ONECEPT Website at ONECEPT GmbH
Development and implementation of the website onecept.at with React/Next.js. Focus on responsive web design, a high-performance component structure, basic SEO optimization, and technical deployment.
Technologies: React, Next.js, TypeScript / JavaScript
Henning Uiterwyk
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
Philipp Steidler
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
Hervé Teguim
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
Farid Alam
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
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.
Philipp Grunert
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
Julian Hillebrand
Last position:
IT Project Manager AI product for automating knowledge-intensive processes at Leading provider of large-scale catering & food services
Project: Concept and implementation of an AI product for four business use cases
Project management of 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 from CRM and document data, voice-based capture and structuring of reports, detection and merging of duplicates in master data, and data-based market analysis. A central focus was a privacy-compliant architecture that passed the internal IT security review and enabled productive use.
- Translating business requirements into clearly defined AI use cases with a clear product scope and clear value proposition
- Selecting and evaluating models and architecture options for text extraction, speech-to-text and context enrichment from business systems, including LLM integration, function calling and retrieval
- Designing and enforcing an architecture with European hosting, data minimization and masking of personal data as a prerequisite for approval
- Managing the interfaces between business, IT, IT security and the external development partner under restrictive data access conditions
- Coordinating with CIO and executive management on data access, risk assessment and approval decisions
- Preparing the transition into productive use
Muhammad Babur
Last position:
Data Engineer | Strategy Consultant at IBM
Responsibilities
- Facilitated financial institutions in Oracle FCUBS upgrade and data migration.
- Developed Core Banking ETL pipelines using Python, SQL, PL/SQL, PySpark and Databricks to ingest, transform and validate transaction data with MLflow for experiment tracking and model lifecycle management.
- Built scalable data solutions using Snowflake, Delta Lake, AWS (Glue, S3, Redshift) and Azure MS Fabric for secure high-performance analytics.
- Orchestrated workflows with Airflow and CI/CD using GitLab for automated deployments monitoring operations.
- Enabled reporting and analytics via Data Warehousing & BI tools (Power BI, Power Query, OBIEE, SSRS) to support compliance, and business decisions.
Jorge Machado
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
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
Moez Seyedan
Last position:
Data Engineer at Loschelder Rechtsanwälte Partnerschaftsgesellschaft mbB
- Designed a future-proof client database for marketing purposes
- Analyzed requirements, designed, and modeled an entity-relationship model
- Consolidated and optimized a client file from various data sources for targeted marketing campaigns
- Worked closely with marketing and IT in an agile environment to iteratively develop the solution
- Technologies and methods: MS Office (mainly Excel), MS Dynamics CRM, MS SharePoint
Anshita Srivastava
Last position:
Business Intelligence Developer and Data Analyst at Deloitte Consulting
Specialize in turning complex data from diverse environments into actionable business value through compelling visual storytelling. I am an expert in generating actionable insights and presenting recommendations to business stakeholders. My technical proficiency in SQL, Python, and leading data visualization tools like Tableau and Power BI allows me to deliver a new generation of self-service tools and analytics services.
- Data Visualization & Storytelling: Created impactful data visualizations and dashboards in Tableau and Power BI, effectively communicating findings and presenting actionable recommendations to C-suite stakeholders and business leaders.
- Stakeholder Management: Built effective working relationships with key business stakeholders, data engineers, and other partners to achieve common data-driven goals and targets.
- Insights & Recommendations: Generated actionable insights from complex data analysis for funnel conversion, marketing performance, and ROI, directly influencing business performance and strategy.
- Data Collaboration & Empowerment: Worked closely with cross-functional teams to support the ongoing data needs of internal partners, helping to optimize internal data processes and workflows.
- BI & Data Expertise: Applied extensive experience in data modeling, data collection, data mining, and analysis to deliver end-to-end analytical solutions from stakeholder discovery to production.
Discover over 15,000 top freelancers
Data Engineer statistics
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.4 years
Positions per freelancer
11
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
90%
Master's degree or higher
60%
Doctorate
9%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
93%
Based on our profile pool as of 21 Aug 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 21 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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 780 € based on an 8-hour working day.
Of the freelancers working as Data Engineer, 90% hold at least a Bachelor's degree, 60% hold at least a Master's degree, and 9% hold a doctorate.
On average, freelancers working as Data Engineer have 16 years of professional experience, with a single engagement typically lasting around 2.4 years.
The most common languages among freelancers working as Data Engineer are English (96%), German (85%), and French (18%).
The most common industries among freelancers working as Data Engineer are Information Technology (91%), Banking and Finance (55%), and Professional Services (39%).
The most common business areas among freelancers working as Data Engineer are Information Technology (100%), Business Intelligence (93%), and Product Development (54%).
FRATCH Data Engineer 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.
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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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
