
Python Expert
in minutes from over 15,000 CVs with precise AI matchingHire experts who build data pipelines, machine learning systems, web services and automation with Python, supported by tools such as Django, FastAPI, pandas and PyTorch. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts who have recently used Python
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company’s BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across departments
- Forecasting & Planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enabling rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing and consistent modeling using Star Schema, DAX and Power Query
- Incident & Anomaly Analysis: Identification, investigation and explanation of data anomalies, including root-cause analysis and specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email delivery of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Christel Kristin R.
Last position:
SAP Senior / Lead Consultant / Team Lead / Trainer at Mercator Leasing GmbH
- Merging several systems, consulting for SAP Finance / Controlling overall, FI/CO, DRC, F2R, R2R, incoming and outgoing invoice mapping and target postings of contracts and BP from source systems. CAMt53, creation of the technical concept for the migration of various business areas, creation of migration templates and FMD Files, support with data migration, consolidations and data entry, test management for the SAP part, test support, training of end users in all FI and CO areas, FI-AA
André G.
Last position:
Financial Reporting Consultant at Client anonymized (services sector)
Financial reporting as an MVP. Follow-up assignment.
- Reports on profit and loss, balance sheet and cash flow
- Budget and personnel metrics (FTE)
- Implementation as an MVP with a fixed scope
Alwin G.
Last position:
IT Interim Manager & AI Strategist
- Founder of CheironX: AI-supported GRC management (ISO 27001, BSI IT-Grundschutz, TISAX, DORA)
- Strategic focus on Agentic AI and GenAI for modern IT Governance, Risk & Compliance Management
- IT interim management and strategic consulting
Michael F.
Last position:
Platform Architect (Project lead) at WĂĽrth IT
I designed and built a self-service AWS platform for Data Science teams with little or no infrastructure-as-code experience. A team writes about 10 lines of Python and gets a deployable stack with full SDLC release cycle that already follows the governance rules. Networking, encryption, IAM and tagging are decided by the platform, not by each team.
New teams or AWS accounts are onboarded with one pull request. Waste prevention and cost control is built into the defaults: Data scientists don't need infrastructure skills.
- Construct library (AWS CDK, Python). Built a versioned catalog of about 20 hardened constructs for being used in data-science projects with ML context
- Governance as code
- Multi-account, multi-tenant
- Golden-path scaffolding
- CI/CD and supply chain. Built reusable GitHub Actions workflows
- Release engineering
- QA with e2e, contract testing and unit test coverage with each release candidate
Dany-Armand D.
Last position:
Senior Data Scientist at ibg NDT GmbH
- Investigate the relationship between Eddy Current Testing (ECT) signals and microstructural properties
- Detect latent patterns in ECT data that reflect intrinsic material characteristics
- Develop and validate predictive models for microstructural classification and quantification, using hardness and case depth as benchmarks
- Apply Bayesian Structural Equation Modeling for advanced data analysis
Lutz E.
Last position:
Freelance Software Developer – GoPro MultiCam Control via COHN at Plutos Company UG
- Developed a pilot system to control and monitor multiple GoPro cameras
- Implemented start, stop, status, media, download and diagnostic functions via COHN endpoints
- Built camera detection, individual and group control, and stable session/KeepAlive logic
- Set up a download pipeline with a queue, retry handling and temporary files
- Ensured consistent session, folder and file naming for multicamera recordings
- Integrated provisioning, certificates and network operations
Michael S.
Last position:
IT & Test Consultant at Testing Experts GmbH
AI Readiness - Part 3: Creating an e-book on the use of AI in software testing
- Designed and created an e-book on the sensible and safe use of AI in software testing from the perspective of an experienced test expert.
- Used generative AI (ChatGPT, Perplexity Pro) as a sparring partner for structuring, wording suggestions and creating variations, with all content finally reviewed and approved using my own expertise.
- Developed practical insights into the opportunities and risks of AI in testing, with a special focus on collaboration between testers and AI instead of “tester vs. AI”.
- Title: “11 starke Gründe: Dream-Team statt Rivalen - Tester und KI sind gemeinsam unschlagbar”
- Subtitle: “Wie Mensch und Maschine im Softwaretest das perfekte Team bilden – und warum reines KI-Testing riskant ist.”
- Planned use of the e-book as a basis for workshops, customer meetings or internal training.
- Technology: Generative AI, ChatGPT, Perplexity Pro, Microsoft Word, Adobe Acrobat, many years of hands-on professional testing experience, common sense.
Franz B.
Last position:
Product Development (AI) at Self-initiated
AI phone assistant platform
Claude Code, Google AI Studio, Python, LLM / Voice-AI, PostgreSQL
- Designed and hands-on developed an AI-powered phone assistant platform (Voice-AI / LLM)—from idea and architecture through to MVP/product.
- Built agentic workflows and end-to-end automations with Claude Code and Google AI Studio.
- Also supported AI-powered delivery in client engagements: using Claude Code for governance documentation, requirements drafts, and automations.
Robert D.
Last position:
Java Software Architect at adesso insurance solutions GmbH
Paul W.
Last position:
Enterprise Architect at ePrimo GmbH
Led the end-to-end integration and migration architecture for decommissioning SAP IS-U market communication and transitioning to Salesforce Marketing Cloud (SFMC) with E.ON IT Services — covering regulated EDIFACT market messaging, a three-phase data migration (transfer of market message inventory after cutover), redesign of market communication processes, and full GDPR and German energy-market compliance.
- Designed the three-tier target architecture (SAP IS-U → NScale B2B gateway → SFMC) processing 50,000–100,000 EDIFACT messages daily within sub-5-minute SLAs.
- Architected the three-phase data migration: 36 months of historical data, zero-loss cutover of in-flight market messages, and near-real-time CDC synchronization.
- Delivered secure, BDEW/GDPR-compliant integration (AS2/OFTP2, RFC/IDoc, REST APIs, OAuth 2.0, Azure AD, AWS S3) achieving >99.5% uptime, <0.1% migration discrepancy, and RTO <4 hours.
- Applied BPMN to model and optimize the market communication processes.
Technologies: SAP IS-U, NScale, Salesforce Marketing Cloud, EDIFACT, OAuth 2.0, Azure AD, AWS S3, BPMN, Jira, Confluence, Draw.io
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Vaios K.
Last position:
Product Manager & Strategic Advisor at Freelance, Lovable, Dreamarium
- Advise early-stage startups on product vision, roadmapping, and GTM strategy, combining PMP and Stanford PM training with hands-on technical execution.
- Lovable: built and led a 200+ member community program for AI-native founders, bridging real-time founder feedback into product roadmap decisions.
- Lead customer discovery and OKR design engagements, scoping AI integration opportunities across client products.
Vladimir E.
Last position:
Technical Project Manager / Freelancer at Deutsche Bank
- Engaged within the Information and Security Management division for a Proof of Concept (PoC) project focused on developing a Client Security Portal.
- Managed project resources across the UK, India, and Germany.
- Architected and deployed the PoC solution leveraging a modern Java stack, Google Cloud Platform (GCP) with Kubernetes, and Active Directory (Entra ID) for authentication.
- Implemented OAuth 2.0 for authorization.
- Ensured DORA alignment according to Deutsche Bank security rules and regulatory preparation.
Niko S.
Last position:
Developing Architect, Technical Lead "gridlytics" at HH Energienetze
- Built a data integration platform for high-, medium- and low-voltage assets to enable contextual analysis of time series with master data from SCADA control systems (IEC 60870 104), INIS and SAP.
- Responsible for the architecture and implementation of the solution, and acted as a sparring partner for the Product Owner.
- Technologies used: Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter and various API gateways.
Discover over 15,000 top freelancers
Statistics of experts using Python
Aggregated from the professional profiles of matched freelancers.
Experience
16 years

Position duration
5.1 years

Positions per freelancer
10

Top business areas
Information Technology, Product Development, Project Management

Top industries
Information Technology, Manufacturing, Automotive

Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
94%
Master's degree or higher
69%
Doctorate
12%

Certifications per freelancer
3

Most common languages
English, German, French

Speak two or more languages
98%
Based on our profile pool as of 5 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 using Python
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 5 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Python 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 (81%)
- Manufacturing (39%)
- Automotive (38%)
- Banking and Finance (37%)
- Education (35%)
- Professional Services (31%)
- Healthcare (28%)
- Retail (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Python’s role
Python is a general-purpose programming language used for web services, data processing, machine learning, automation and scientific software. Its readable syntax and broad standard library make it suitable for prototypes, internal tools and production systems. CPython is the reference implementation most teams use.
What it builds
Python supports products and workflows across many technical environments:
- APIs and web applications with Django, Flask or FastAPI
- Data pipelines, analysis tools and reporting systems
- Machine learning and artificial intelligence services
- Test automation, scripts and internal productivity tools
- Scientific, financial and research software
Ecosystem and tooling
Strong Python work depends on more than language syntax. Professionals use pandas and NumPy for data, PyTorch or scikit-learn for modeling, and Celery for background processing. They also work with PostgreSQL, Redis, Docker, Git, CI pipelines and cloud services, while managing environments with uv, Poetry or virtualenv.
When expertise matters
Companies bring in freelance Python expertise when a data workflow is unreliable, an API needs to scale, or a prototype must become maintainable production software. Specialists can also modernize legacy scripts, connect third-party services, improve test coverage and establish deployment practices. Clear scope, ownership of existing code and access to relevant systems help remote collaboration run smoothly.
What strong specialists deliver
A capable Python professional chooses simple structures before adding complexity. They define interfaces, isolate dependencies and write tests that protect important behavior. They understand performance, security, observability and error handling, then document decisions so other teams can operate the result. Experience with the relevant framework and deployment environment matters as much as language fluency.
Selecting the right fit
Review a specialist’s experience with the exact kind of system you need, not just a list of Python libraries. Ask how they test data quality, handle secrets, monitor services and manage changes in shared repositories. A practical assessment should examine a small, relevant task and the reasoning behind it. Communication style, written clarity and comfort with your team’s working language are equally important.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Python.
Python is used for web APIs, data analysis, machine learning, automation, scientific computing and internal business tools. Its ecosystem lets teams use one language across experimentation, integration and production services.
Python usually offers faster development and a strong data and machine learning ecosystem. JavaScript is central to browser applications, Java is common in large enterprise systems, and Go is often selected for compact, high-concurrency services. The right choice depends on runtime needs, team skills and available libraries.
Python work often connects with SQL, REST and GraphQL APIs, Docker, Git, Linux, cloud services and CI pipelines. Depending on the project, useful adjacent skills include Django or FastAPI, pandas, NumPy, PyTorch, Kubernetes and message queues.
Python project needs vary with risk and scope rather than a fixed tenure. A small automation task may need focused scripting and testing skills, while a regulated data product or distributed API requires architecture, security, observability and deployment experience.
Python specialists can work remotely when the team provides repository access, clear interfaces, test data and dependable communication routines. Shared documentation, code review and predictable release practices matter more than physical proximity for many projects.
Python web projects often use Django when they need an integrated framework with authentication, administration and database support. Flask suits smaller, modular services, while FastAPI is a strong option for typed APIs and asynchronous workloads. The decision should follow the product’s boundaries and operational needs.
Python quality shows in readable structure, meaningful tests, safe dependency management and clear error handling. Ask a specialist to explain trade-offs, demonstrate how they would monitor the system and review a relevant code sample for maintainability rather than cleverness.
Python professionals are expected to work beyond isolated scripts. Production assignments commonly involve version control, packaging, testing, databases, APIs, deployment and collaboration with product or data teams. Familiarity with the project’s domain and the ability to explain technical decisions are valuable.
The average hourly rate of freelancers who have used Python in their recent projects is 90 €, which corresponds to a daily rate of about 723 € based on an 8-hour working day.
Of the freelancers who have used Python in their recent projects, 94% hold at least a Bachelor's degree, 69% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers who have used Python in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 5.1 years.
The most common languages among freelancers who have used Python in their recent projects are English (98%), German (97%), and French (18%).
The most common industries among freelancers who have used Python in their recent projects are Information Technology (81%), Manufacturing (39%), and Automotive (38%).
The most common business areas among freelancers who have used Python in their recent projects are Information Technology (90%), Product Development (79%), and Project Management (51%).
Main locations of FRATCH Experts, who have recently used Python
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.
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!

Berlin
Hamburg
Munich
Cologne
Frankfurt
Stuttgart
Dusseldorf
Leipzig
Dortmund
Essen
Bremen
Dresden
Hanover
Nuremberg
Vienna
Graz
Zurich
Basel