Jupyter Experts in Frankfurt
for data workflows, matched in minutes with vetted and available freelancersHire experts who turn exploratory analysis into reliable notebooks, data pipelines and interactive dashboards with JupyterLab, Python and connected analytics tools. FRATCH matches you quickly and precisely with vetted, available freelancers.
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.
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.
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
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
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.
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
20 years (Germany: 15 years)
Position duration
1.9 years (Germany: 2.8 years)
Positions per freelancer
18 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Banking and Finance, Information Technology, Pharmaceutical
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
50% (Germany: 80%)
Doctorate
25% (Germany: 20%)
Certifications per freelancer
6 (Germany: 3)
Most common languages
German, English, Russian
Speak two or more languages
100% (Germany: 99%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 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 19 Sep 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 (75%)
- Information Technology (75%)
- Pharmaceutical (63%)
- Education (50%)
- Retail (50%)
- Automotive (38%)
- Healthcare (38%)
- Professional Services (38%)
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, explanations, visualizations and results in one shareable document. Companies use it to explore data, test models, document research and communicate findings across technical and business teams.
JupyterLab Ecosystem
JupyterLab provides a flexible workspace for notebooks, terminals, text files and data views. The broader ecosystem includes IPython, Jupyter Notebook, widgets, kernels and extensions for languages beyond Python. Strong specialists connect these components with pandas, NumPy, Matplotlib, scikit-learn and cloud storage.
Practical Deliverables
Jupyter work often supports decisions before a production system is built. Typical deliverables include:
- Reproducible notebooks for analysis and reporting
- Exploratory data workflows and visual explanations
- Prototypes for machine learning and forecasting
- Interactive widgets for scenario testing
- Documentation for research and internal handover
When Expertise Helps
Companies bring in freelance Jupyter expertise when analysis is slow, notebooks are difficult to reproduce or prototypes must become dependable workflows. Specialists can structure messy notebooks, separate reusable code from exploration and connect analysis with databases, APIs, version control and deployment processes. In Frankfurt, this can support finance, manufacturing, logistics, research and other data-intensive teams while fitting remote or on-site collaboration.
Quality Signals
A strong professional treats a notebook as part of a wider system, not as an isolated document. Look for clear assumptions, controlled environments, meaningful tests, readable visualizations and documented data sources. Useful specialists also understand security, access permissions, performance and the path from Jupyter exploration to scheduled or production-grade execution.
Collaboration and Handover
Successful projects define the purpose of each notebook, the data it may access and the audience for its results. Specialists should agree on repository practices, environment setup, review routines and handover documents early. Remote collaboration works well when notebooks, datasets, credentials and decisions are managed in shared, traceable workflows, with clear communication in the required languages.
Frequently asked questions
Key details about Jupyter, drawn from the questions we get asked most.
Jupyter is used for interactive data analysis, visualization, research, machine learning experiments and technical reporting. Teams can inspect results step by step, explain decisions beside the code and share a reproducible record with colleagues.
JupyterLab is the broader interface for working with notebooks, files, terminals, data views and extensions in one workspace. Jupyter Notebook focuses on the notebook document itself, while both use kernels to run code interactively.
A strong Jupyter specialist often works with Python, pandas, NumPy, SQL, Git and visualization libraries. Depending on the project, knowledge of cloud storage, machine learning, APIs, container workflows or orchestration is also valuable.
The right level depends on the deliverable, data complexity and need for production handover. For a serious engagement, look for a Jupyter professional who has handled comparable data sources, documented assumptions and moved exploratory work into maintainable processes.
Yes. Jupyter projects are well suited to remote collaboration when repositories, environments, datasets and access rules are clearly managed. A specialist in Frankfurt can work on-site or remotely, provided communication, review and language expectations are agreed at the start.
Jupyter is a good fit when the analysis needs flexible code, evolving hypotheses, detailed explanation or experimental models. A dashboard tool may be better for fixed recurring views, while Jupyter can prepare the analysis that later feeds a formal reporting solution.
Review whether Jupyter notebooks run from a documented environment and produce the same results from a clean start. Also check data lineage, validation, readable structure, useful visualizations, access controls and whether another professional could maintain the work.
A Jupyter professional should clarify the business question, data ownership, target audience, security constraints and expected handover. It is also important to agree whether the outcome is an exploratory notebook, a reusable analysis package, a teaching resource or a production-connected workflow.
The average hourly rate of freelancers in Frankfurt, Germany who have used Jupyter in their recent projects is 100 €, which corresponds to a daily rate of about 801 € 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, 50% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Jupyter in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 1.9 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 (25%).
The most common industries among freelancers in Frankfurt, Germany who have used Jupyter in their recent projects are Banking and Finance (75%), Information Technology (75%), and Pharmaceutical (63%).
The most common business areas among freelancers in Frankfurt, Germany who have used Jupyter in their recent projects are Information Technology (100%), Business Intelligence (88%), and Product Development (75%).
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 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
