Data Engineers in Frankfurt
in minutes from over 15,000 CVs with the power of AI.Bring in Data Engineers for warehouse design, ETL and ELT pipelines, streaming data flows, and cloud analytics setups. FRATCH matches you quickly with vetted, available freelancers who can start on the work that matters.
Meet FRATCH Data Engineers
Basil Sattler
Senior Developer / Data Engineer
Last position:
Senior Developer / Data Engineer at Large energy-sector company
- Co-founded the Real-Time Data team, which grew to 10 members over time.
- Developed and delivered core data products.
- Optimized real-time application performance and implemented monitoring, alerting and logging solutions to ensure system stability.
- Created and maintained deployment pipelines.
- Collaborated with teammates, architects and experts in an agile Scrum environment.
- Operated applications, analyzed, tested and troubleshot software solutions.
Ashkan Zadeh
Microsoft Azure Senior Data Engineer / Senior Data Scientist
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Ashkan Zadeh
Microsoft Azure Senior Data Engineer / Senior Data Scientist
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Ulm Paunel
Freelance IT Specialist
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Ulm Paunel
Freelance IT Specialist
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Eduard Van Kleef
Workshop Leader 'Introduction to AI Development Tools'
Last position:
Workshop Leader 'Introduction to AI Development Tools' at Software company in Wiesbaden
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
Eduard Van Kleef
Workshop Leader 'Introduction to AI Development Tools'
Last position:
Workshop Leader 'Introduction to AI Development Tools' at Software company in Wiesbaden
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
Roman Krivtsov
Senior Data Engineer / Cloud Architect
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Roman Krivtsov
Senior Data Engineer / Cloud Architect
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Polina Schulz
Data Migration Lead – Process Automation, Data Engineering & Reporting
Last position:
Data Migration Lead – Process Automation, Data Engineering & Reporting at Large Public-Sector Bank
Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Polina Schulz
Data Migration Lead – Process Automation, Data Engineering & Reporting
Last position:
Data Migration Lead – Process Automation, Data Engineering & Reporting at Large Public-Sector Bank
- Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Jörg-Ulrich Hammerbacher
BigData Engineer
Last position:
Data flows for health insurance providers
- Further development and creation of data flows for health insurance providers
- Data management across various storage systems (DB2, MSSQL, PostgreSQL, S3, custom APIs, ...)
- Documentation and training
- Planning and deployment of NiFi 2.x (major upgrade)
- Integrating Grafana for visualization, monitoring, and alerting
- Extensive use of the NiFi API to continuously monitor the system and its components
Jörg-Ulrich Hammerbacher
BigData Engineer
Last position:
Data flows for health insurance providers
- Further development and creation of data flows for health insurance providers
- Data management across various storage systems (DB2, MSSQL, PostgreSQL, S3, custom APIs, ...)
- Documentation and training
- Further development and creation of data flows for health insurance providers
- Data management across various storage systems (DB2, MSSQL, PostgreSQL, S3, custom APIs, ...)
- Documentation and training
- Planning and deployment of NiFi 2.x (major upgrade)
- Integrating Grafana for visualization, monitoring, and alerting
- Extensive use of the NiFi API to continuously monitor the system and its components
- Planning and deployment of NiFi 2.x (major upgrade)
- Integrating Grafana for visualization, monitoring, and alerting
- Extensive use of the NiFi API to continuously monitor the system and its components
Discover over 15,000 top freelancers
Data Engineers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
1.9 years
Positions per freelancer
15
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Energy, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
75%
Doctorate
25%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100%
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. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Average rates for Data Engineers & Seniority distribution
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.
Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the role
Data flows
Data Engineers build the pipelines that move raw data into systems teams can trust. They collect, clean, transform, and load data from apps, APIs, databases, and files into warehouses, lakes, and analytics platforms. The result is usable data for reporting, product teams, and machine learning work.
- Design ETL and ELT pipelines
- Build batch and streaming data flows
- Model data for analytics use
- Improve reliability, quality, and traceability
Core stack
Strong Data Engineers work across SQL, Python, orchestration tools, and cloud data services. They know how to structure data models, manage dependencies, and keep pipelines maintainable. In many projects they also work with Spark, dbt, Airflow, Kafka, Snowflake, BigQuery, or Azure Data Factory, depending on the client stack.
- SQL for querying and transformations
- Python for data logic and automation
- Cloud platforms and warehouse tooling
- Monitoring, logging, and data quality checks
When to hire
Companies bring in freelance Data Engineers when a data platform needs to be built, repaired, or scaled without slowing the team down. This is common during warehouse migrations, dashboard rebuilds, product analytics rollouts, and new source-system integrations. In Frankfurt, banks, insurers, logistics firms, and enterprise software teams often need this support for complex data landscapes and tight internal coordination.
- New platform or migration project
- Broken or slow pipelines
- Missing data models or poor quality
- Temporary capacity for an internal team
What good looks like
A strong Data Engineer does more than move data. They understand source systems, business logic, and downstream use. They document clearly, choose simple solutions where possible, and build with maintainability in mind. Good freelancers also align early with analysts, backend teams, and stakeholders so the pipeline design fits the real use case.
Tools and specializations
The role can focus on different layers of the stack. Some Data Engineers specialize in warehouse architecture and dimensional models. Others focus on cloud migration, real-time data processing, or analytics engineering. The best fit depends on whether the job is mostly integration, transformation, platform design, or operational support.
- Cloud data engineering
- Warehouse and lakehouse design
- Streaming and event-driven data
- Analytics engineering and dbt work
Working model
Freelance Data Engineers are often useful when a project has a clear scope and a deadline, but the company does not need a permanent hire yet. They can join remotely for most tasks or work on-site in Frankfurt when access to internal teams, systems, or security processes requires it. Clear requirements, source access, and a defined owner help them deliver faster.
Frequently Asked Questions
Have questions? See our quick guide to FRATCH
A Data Engineer builds and maintains the data paths that move information from source systems into analytics and reporting environments. That usually includes ingestion, transformation, validation, and pipeline monitoring. In practice, they help teams turn scattered data into something reliable enough for business use.
Look for strong SQL, practical Python, and experience with orchestration and cloud data tools. A good candidate also understands data modelling, testing, and how to keep pipelines stable under change. For many jobs, it matters just as much that they can document work clearly and talk to analysts and backend teams.
A Data Engineer is mainly responsible for getting data into the right place, in the right shape, and with the right quality. A data analyst works more on interpretation and reporting, while an analytics engineer usually focuses more on transformation inside the warehouse. The boundaries can overlap, but the core ownership is different.
A freelancer is often the better choice when the work is project-based, urgent, or tied to a migration or platform change. It also helps when your internal team needs extra capacity without a long hiring process. For a clear build, fix, or handover, freelance support is often the most efficient option.
Many Data Engineers can work remotely once access, security, and requirements are in place. On-site time in Frankfurt can help when the project depends on close coordination with data owners, infrastructure teams, or regulated environments. The right setup depends on the client’s systems and the level of internal access needed.
Common tools include SQL, Python, Airflow, dbt, Spark, Kafka, and cloud platforms such as Snowflake, BigQuery, and Azure Data Factory. The exact stack depends on whether the job is batch processing, streaming, warehouse design, or migration work. A strong freelancer adapts to the stack instead of forcing one preferred tool.
A solid Data Engineer delivers working pipelines, clean data models, clear documentation, and sensible monitoring or testing. You should also expect a handover that makes the setup maintainable for the team after the project ends. If the role includes migration work, the deliverable should also include a stable cutover plan and validation steps.
Ask for examples that show how the candidate handled source-system complexity, data quality issues, and changes in business logic. Good signs are careful questions, clear trade-offs, and a practical plan for testing and monitoring. For this role, quality is less about flashy tooling and more about whether the data stays correct, traceable, and easy to operate.
The average hourly rate for Data Engineers in Frankfurt is 110 €, which corresponds to a daily rate of about 880 € based on an 8-hour working day.
Of the freelancers working as Data Engineers in Frankfurt, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers working as Data Engineers in Frankfurt have 20 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers working as Data Engineers in Frankfurt are German (100%), English (100%), and French (29%).
The most common industries among freelancers working as Data Engineers in Frankfurt are Information Technology (86%), Energy (71%), and Banking and Finance (71%).
The most common business areas among freelancers working as Data Engineers in Frankfurt are Business Intelligence (100%), Information Technology (100%), and Product Development (71%).
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.
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
