Jupyter Experts in Frankfurt
matched in minutes by AIHire experts who turn exploratory analysis into reliable notebooks, reusable data workflows and clear visualizations with Jupyter, Python and connected data tools. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Frankfurt, who have recently used Jupyter
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Michael W.
Last position:
Business Analyst, Product Owner, Deputy Chairman of the Advisory Board at Federal Ministry, large German city
- We designed a networking platform to improve cooperation and information in the district (Project 71).
- We carried out a tender, defined necessary documents and processes.
- We aimed to ensure that the commissioned service provider carries out quality assurance of the project team's results before implementation.
- We selected a provider.
Mathew D.
Last position:
Data Science Expert and AI Strategist at Freelancer
- Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
- Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
- Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
- Utilized OBS and professional audio equipment to ensure high-quality video and audio content
- Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
- Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation
Polina S.
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.
Kurt R.
Last position:
Lead Solution Architect (AI HealthTech) / interim CTO & Product Co-Owner at Physio-Agil Frankfurt
- General CTO responsibilities (architectural design, operational setup, external runtime product evaluation, investor buy-in, regulatory compliance).
- Software development oversight (implementation on deep-dive-in) plus workflow design.
- Product co-ownership.
- Tech/tools/frameworks: proprietary software (Java, JavaScript), Kubernetes, Postgres, MiniIO, Ollama (internal), several xAI API (external), OpenTofu (Terraform), Keycloak, Kafka, Prometheus, ELK Stack, GitHub, GitHub Workflows, Argo CD, ISO 27001, BSI-ISM, EU AI Act.
Olusina F.
Last position:
Cyber Job Simulation at Deloitte Australia
- Completed a job simulation involving reading web activity logs.
- Supported a client in a cybersecurity breach.
- Answered questions to identify suspicious user activity.
Yevgeniy Ă–.
Last position:
Tester, Test & Data Analyst at NORD/LB
- Analyze system requirements and mapping concepts (ETL requirements) for data flows and transformation logic in the bank's DWH
- Analyze data in DB tables and views of the DWH using SQL (DB2)
- Independently define, create, and execute test cases in JIRA Xray (SIT)
- Write SQL queries in DB2 to verify data scenarios and mappings
- Create test plans for SAP FSDP and concurrent projects
- Conduct error and root cause analyses in coordination with business analysts, developers, test managers, and the infrastructure team
- Thoroughly document test results in JIRA Xray
- Coordinate between business analysis, development, DB infrastructure, business units, and external vendors
Rashid I.
Last position:
Java Developer at IT company
- Data transformations
- IT company with more than 100 employees
- Software production
- Data augmentation and normalization, image transformation, format conversion, merging data from multiple sources
- Toolset: Java, Helm, Kubernetes, Kafka, OpenCV, IntelliJ IDEA, Gradle, Git, Docker, Containers, Scrum
Discover over 15,000 top freelancers
Statistics of experts using Jupyter
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 15 years)
Position duration
1.8 years (Germany: 2.8 years)
Positions per freelancer
16 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Banking and Finance, Information Technology, Pharmaceutical
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
56% (Germany: 80%)
Doctorate
33% (Germany: 21%)
Certifications per freelancer
8 (Germany: 3)
Most common languages
German, English, Russian
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Frankfurt 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 of experts in Frankfurt using Jupyter
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Jupyter experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Banking and Finance (78%)
- Information Technology (78%)
- Pharmaceutical (56%)
- Retail (56%)
- Education (44%)
- Professional Services (44%)
- Advertising (33%)
- Automotive (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Notebook-based analysis
Jupyter is an open-source environment for interactive computing. Its notebooks combine executable code, results, visualizations, equations and explanatory text in one shareable document. Companies use Jupyter Notebook and JupyterLab to explore data, test ideas and communicate analytical work clearly.
Data and research workflows
Jupyter supports work across data science, research, finance, engineering and education. Specialists use it to inspect datasets, prototype models, document experiments and present findings. It is useful when people need to see both the reasoning and the output behind an analysis.
- Explore and clean structured or unstructured data
- Build visual analyses and exploratory reports
- Prototype machine learning and statistical models
- Document repeatable research workflows
Python and the ecosystem
Many Jupyter projects use Python with pandas, NumPy, SciPy, scikit-learn and visualization libraries such as Matplotlib or Plotly. Specialists may also work with R, Julia, SQL, Spark and cloud data services through kernels and connectors. JupyterLab provides a broader interface for notebooks, terminals, files and extensions.
Production-ready delivery
A notebook is often the starting point rather than the final deliverable. Strong professionals structure code for reuse, manage environments, connect trusted data sources and turn exploratory work into scripts, pipelines, dashboards or services. They use version control, testing and documentation to reduce the risk of notebooks that cannot be reproduced.
When freelance expertise helps
Companies often bring in external Jupyter expertise when an internal team needs faster analysis, a research process needs stronger documentation or a prototype must become a maintainable workflow. Frankfurt teams can collaborate on-site, remotely or in a hybrid setup, depending on data access and stakeholder needs. Clear English communication is common in international projects.
- Several notebooks need consistent structure and review
- Results must be reproducible across environments
- A prototype needs to connect to operational data
- Analysts need a practical workflow for sharing insights
What strong specialists bring
The best Jupyter specialists combine analytical thinking with software discipline. They choose suitable kernels and libraries, explain assumptions, handle missing or sensitive data carefully and make visualizations easy to interpret. They also know when a notebook is appropriate and when the result belongs in a tested pipeline, application or reporting system.
Frequently asked questions
Key details about Jupyter, drawn from the questions we get asked most.
Jupyter is used for interactive data analysis, research, visualization, teaching and machine learning experiments. A notebook keeps code, outputs and explanations together, which makes exploratory work easier to review and share.
JupyterLab is a broader interface for working with notebooks, files, terminals and extensions in one workspace. Jupyter Notebook offers a more focused notebook experience, while both use the same core interactive computing model.
A strong Jupyter specialist often works with Python, pandas, NumPy, SQL and visualization libraries. Depending on the project, knowledge of cloud storage, Spark, machine learning, Git, testing and data pipeline design is also valuable.
The right Jupyter experience depends on the deliverable. A short exploratory analysis may need strong library knowledge and data judgment, while a shared research environment or production handoff also requires environment management, version control, testing and documentation.
Most Jupyter work can be completed remotely when data access, security requirements and collaboration routines are clear. Frankfurt companies may prefer occasional on-site sessions for workshops, stakeholder reviews or access to restricted systems.
Jupyter is a strong choice when the analysis requires flexible exploration, transparent calculations or written research context. A dashboard tool is usually better for stable, recurring views aimed at business users who need consistent monitoring rather than interactive investigation.
Ask a Jupyter specialist to show how they structure notebooks, explain assumptions and make results reproducible. Look for clear code, meaningful visualizations, managed dependencies, sensible data handling and a plan for turning useful exploration into maintainable work.
A Jupyter assignment may involve much more than writing notebook cells. Clarify the data sources, access rules, expected audience, handoff requirements and whether the output is a research document, reusable workflow, automated pipeline or production component.
The average hourly rate of freelancers in Frankfurt, Germany who have used Jupyter in their recent projects is 105 €, which corresponds to a daily rate of about 844 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Jupyter in their recent projects, 100% hold at least a Bachelor's degree, 56% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Jupyter in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Frankfurt, Germany who have used Jupyter in their recent projects are German (100%), English (100%), and Russian (22%).
The most common industries among freelancers in Frankfurt, Germany who have used Jupyter in their recent projects are Banking and Finance (78%), Information Technology (78%), and Pharmaceutical (56%).
The most common business areas among freelancers in Frankfurt, Germany who have used Jupyter in their recent projects are Information Technology (100%), Business Intelligence (89%), and Product Development (78%).
Main locations of FRATCH Experts, who have recently used Jupyter
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 Hamburg Munich Cologne 
Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin 
Munich 
Dusseldorf Countries:
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
