pytest Expert in Berlin
for reliable test automation, matched in minutes with vetted freelancersHire experts who build maintainable Python test suites, integrate pytest with CI pipelines, and validate APIs and distributed services. FRATCH connects you with precise matches from vetted, available freelancers quickly.
Meet FRATCH Experts in Berlin, who have recently used pytest
Murad H.
Last position:
Founder & Technical Lead at Hubpoint.Ai
- Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
- Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
- Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
- Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
- Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.
Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker
Victor O.
Last position:
AI Training Engineer at Confidential AI Research Client
- Codebase Evaluation & Problem Design: Designed and stress-tested complex software engineering problems against large open-source Python codebases (including pandas), requiring deep context acquisition and architectural understanding to produce well-scoped, realistic problem statements aligned to strict correctness guidelines.
- Agent Failure Analysis: Assessed LLM coding agent solutions for correctness and completeness, identifying meaningful failures across edge case handling, dtype behaviour, and multi-column NaN propagation logic; documented findings with precision for downstream evaluation use.
- Programmatic Test Suite Development: Authored comprehensive pytest suites to programmatically verify agent-generated solutions against defined requirements, with deliberate coverage of boundary conditions and failure modes not caught by naive implementations.
- Containerised Environment Engineering: Built and debugged Docker environments for reproducible agent execution, including git-based repository provisioning, dependency pinning with npm ci, and multi-stage Dockerfile authoring across Linux-based containers.
Mario J.
Last position:
Architect / Developer at handyhase
- Overhaul of the entire design and implementation of new UI/UX aspects using React
- Planning of the software architecture and structuring of the project for long-term scalability using Git for version control
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, maintained the product backlog
- Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural
Tobias J.
Last position:
Design of an AI-Agent-Based ERP System
- Design of an LLM-based agent system to control the ERP software
- Development of agent workflows with LangGraph and PydanticAI
- Planning interfaces between business logic and language models
- Planning agent orchestration
- Prototype development and demonstration
Tools: Python, Pydantic, React, LangChain, LangGraph, Linux
Anuja C.
Last position:
Embedded Software Engineer at Digital Core Technology
- Designed and implemented embedded firmware in C/C++ for ARM-based microcontrollers and SoCs (STM32, NXP, Infineon)
- Worked across bare-metal, FreeRTOS, and Embedded Linux environments, contributing to BSP-level functionality, driver bring-up, and system integration
- Built, configured, and validated Embedded Linux systems using Yocto, including image build, boot validation, and runtime debugging
- Developed and executed unit, integration, and system-level tests, including HIL and SIL test scenarios on real hardware and simulated environments
- Created Python and Bash scripts to automate build, test execution, simulation runs, and reporting within CI/CD pipelines (Jenkins, GitLab CI)
- Designed test cases from software and system-level requirements, covering normal operation, edge cases, and failure scenarios
- Performed low-level debugging using JTAG/GDB, analyzing boot issues, timing problems, interrupts, and peripheral behavior
- Developed and validated communication interfaces including UART, SPI, I2C, CAN using logs and external measurement tools
- Used simulation and virtual test environments to validate software behavior prior to hardware availability
- Read and interpreted hardware schematics to understand signal routing, pin multiplexing, and peripheral connections
- Supported PCB design and review activities using Altium tool, assisting with component selection, pin mapping, and bring-up readiness
- Collaborated with cross-functional hardware, firmware, and system teams to clarify requirements, document assumptions, and improve overall test coverage
- Developed web interfaces using HTML, CSS, and JavaScript (ES6) and basic REST API development with Node.js
- Experience testing web applications and building UI automation frameworks using Selenium WebDriver
Fares K.
Last position:
Research Assistant – AI & Computer Vision at Iris-Sensing GmbH
- Designed and implemented a real-time perception pipeline using YOLOv7 on Time-of-Flight (ToF) sensor data, enabling live streaming, inference, and on-frame visualization for passenger detection.
- Fine-tuned and evaluated multiple state-of-the-art monocular depth estimation models for Automatic Passenger Counting (APC), and developed a custom hybrid depth model that improved depth accuracy in challenging scene regions.
- Demonstrated that model-generated depth maps outperform raw sensor depth for APC tasks across several datasets, contributing to measurable reductions in counting error.
Maurizio F.
Last position:
Python Software Developer at Schönhofer Sales and Engineering GmbH
- Implemented a command line interface (CLI) for integration of REST APIs of various microservices for end users
- Centralized and simplified interaction with services through the CLI
- Implemented a REST microservice for custom data schemas based on an API-first approach
- Developed event-driven control with RabbitMQ to connect to other services
- Deployed services using Docker and Kubernetes and extended the CLI
- Managed complexity and data volume handling through the microservice
Meisam G.
Last position:
Machine Learning Engineer at Geeks
- Utilized a Large Language Model (LLM) at WordUp, tailored to enhance vocabulary learning by understanding and generating contextual examples, improving personalized learning experiences
- Developed a high-performance Fast API service for retrieving high-K similar vectors with batch querying capabilities. This service is crucial for enabling efficient Retrieval Augmented Generation (RAG) and semantic search applications
- Designed and implemented a high-performance Python ETL pipeline, optimizing CPU and I/O utilization and streamlining data cleansing logic, resulting in a 30% reduction in processing time
- Utilized machine learning to analyze user behavior and predict churn, identifying key engagement trends that led to a 15% increase in user retention and satisfaction
- Developed a Customer Lifetime Value (CLTV) prediction model, leading to a 10% increase in average CLTV through targeted retention efforts
Discover over 15,000 top freelancers
Statistics of experts using pytest
Aggregated from the professional profiles of matched freelancers.
Experience
13 years (Germany: 14 years)
Position duration
2.1 years (Germany: 1.8 years)
Positions per freelancer
9 (Germany: 10)
Top business areas
Information Technology, Product Development, Quality Assurance
Top industries
Information Technology, Education, Healthcare
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
88% (Germany: 73%)
Doctorate
13% (Germany: 12%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, Arabic
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 Berlin 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 Berlin using pytest
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.
pytest 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 (100%)
- Education (44%)
- Healthcare (44%)
- Automotive (33%)
- Government and Administration (33%)
- Biotechnology (22%)
- Energy (22%)
- Professional Services (22%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What pytest does
pytest is a testing framework for Python. It helps teams write clear tests for functions, classes, APIs and complete application workflows without requiring extensive boilerplate. Its readable assertions, fixtures and failure reports support fast feedback during development and release work.
Core building blocks
Tests are usually plain Python functions whose names follow pytest discovery rules. Fixtures provide reusable setup and cleanup, while parametrization runs the same behavior against varied inputs. Markers help organize suites, select test groups and separate slower integration checks from focused unit tests.
Ecosystem and tooling
pytest works with the wider Python quality toolchain and can be extended through plugins.
- Integrate pytest with tox, nox or project-specific test commands
- Run coverage analysis with coverage.py and pytest-cov
- Test HTTP services with requests, httpx or pytest-asyncio
- Connect reports and test execution to CI systems
- Replace external services with mocks, stubs and controlled fixtures
Where companies use it
Teams use pytest for backend services, data processing, automation tools and Python libraries. It also supports API contracts, database behavior, event-driven workflows and browser-facing service layers when paired with suitable tools. In Berlin, companies may need specialists who can collaborate with local product teams while working remotely across Germany.
When to bring in expertise
Freelance support is useful when a codebase has slow or unreliable tests, weak coverage of critical paths or no consistent test structure. Specialists can establish a test strategy, refactor brittle fixtures, add regression coverage and make suites reliable in CI. They can also review test design before a major migration or product release.
What strong specialists deliver
Strong pytest professionals understand Python internals, application boundaries and the risks behind different test levels. They keep tests isolated, deterministic and easy to diagnose instead of merely increasing test volume. Look for experience with mocking decisions, asynchronous code, databases, containers, parallel execution and readable failure reporting, plus the ability to explain trade-offs to the wider team.
Frequently asked questions
Need clarity? These are the questions we hear most often about pytest.
pytest is used to test Python code at unit, integration and end-to-end levels. Companies use it to verify business rules, APIs, database interactions, asynchronous workflows and regression cases with readable tests and detailed failure output.
pytest generally requires less boilerplate than Python’s built-in unittest and offers fixtures, parametrization and a broad plugin ecosystem. unittest remains a sound choice for teams invested in its class-based style, while pytest can also run many existing unittest tests.
A strong pytest specialist usually understands Python packaging, Git workflows and CI systems. Useful adjacent skills include coverage.py, mocking, SQL databases, Docker, HTTP clients, asynchronous programming and tools such as tox or nox.
The right level depends on the codebase, risk and test architecture rather than a fixed amount of experience. A smaller unit-test task may need focused Python testing skills, while a large suite benefits from someone who has designed fixtures, stabilized CI and handled integration boundaries.
pytest is well suited to remote collaboration because tests, fixtures and CI results are shared through the code repository and build pipeline. Berlin teams should agree on review practices, documentation, working hours and communication language before work begins, especially when specialists are not on site.
pytest is a strong fit when a team wants concise test code, flexible fixtures and easy extension through plugins. Other tools may suit projects that require a strict built-in structure or a specific framework integration, so the decision should follow the existing stack and maintenance needs.
pytest can test APIs with clients such as requests or httpx and can support asynchronous code through plugins such as pytest-asyncio. Quality depends on careful fixture scope, controlled test data and clear separation between fast service tests and external integration checks.
Ask a pytest professional to explain how they would isolate dependencies, manage fixtures and investigate a flaky test. Review whether their tests express business behavior clearly, fail for useful reasons and run reliably in CI, rather than judging quality by test volume alone.
The average hourly rate of freelancers in Berlin, Germany who have used pytest in their recent projects is 86 €, which corresponds to a daily rate of about 686 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used pytest in their recent projects, 100% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Berlin, Germany who have used pytest in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Berlin, Germany who have used pytest in their recent projects are German (100%), English (100%), and Arabic (11%).
The most common industries among freelancers in Berlin, Germany who have used pytest in their recent projects are Information Technology (100%), Education (44%), and Healthcare (44%).
The most common business areas among freelancers in Berlin, Germany who have used pytest in their recent projects are Information Technology (89%), Product Development (78%), and Quality Assurance (78%).
Main locations of FRATCH Experts, who have recently used pytest
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 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
