pytest Experts in Berlin
in minutes from over 15,000 CVs with the power of AIHire experts who write clear test suites, build reliable fixtures and mocks, and improve CI test runs with pytest, pytest-cov, and parametrized testing. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used pytest
Victor Omojoye
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 Jelinski
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 Dhamo
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 Jaeuthe
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 Chandran
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 Kallel
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 Fleischer
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 Ghafarlangroudi
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: 15 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, Automotive
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
86% (Germany: 73%)
Doctorate
14% (Germany: 10%)
Certifications per freelancer
0 (Germany: 2)
Most common languages
German, English, Arabic
Speak two or more languages
100% (Germany: 99%)
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Test coverage
pytest is a Python testing framework for unit, integration, and end-to-end checks. Teams use it to catch regressions early, document expected behavior, and keep release work stable. It fits services, APIs, data pipelines, and internal tools built in Python.
What teams build
Common work with pytest includes:
- test suites for new Python features
- regression checks for existing code
- fixture setup for repeatable test data
- parametrized tests for many input cases
- CI pipelines that run tests on every change
Ecosystem
Strong specialists know more than plain asserts. They use fixtures, markers, plugins, and output capture to keep tests readable and fast. Common additions include pytest-cov for coverage, pytest-mock for mocking, and plugins for async code or browser flows.
When to hire
Companies bring in freelance help when a codebase has weak tests, slow pipelines, or flaky checks that block releases. Berlin teams often need support during refactors, migrations, or feature work where test design must keep pace with delivery. Clean test structure pays off fast.
What good experts do
Good professionals do not only add tests. They spot test gaps, remove brittle setup, and choose the right scope for each check. They also keep names clear, failures easy to read, and test files close to the code they protect.
Working style
pytest work can be done fully remote, which suits distributed Python teams well. On-site time in Berlin helps when experts need to pair on legacy suites, release hardening, or shared coding standards. Clear test goals and access to the existing CI setup matter most.
Frequently asked questions
Need clarity? These are the questions we hear most often about pytest.
pytest is used to test Python code with clear, readable tests. Companies rely on it for unit tests, integration checks, API validation, and regression protection. It is a strong fit when a team wants simple test code with powerful fixtures and plugin support.
pytest is usually preferred when teams want less boilerplate and more flexible test style. Compared with unittest, it makes assertions and fixtures easier to read, while still running standard Python tests. unittest can be fine for very small or highly traditional codebases, but pytest often scales better for active product work.
pytest is often the natural successor for teams that still have old nose-based test suites. nose is no longer the usual choice for new work, while pytest has a larger ecosystem and active use in modern Python projects. A freelancer can often migrate tests gradually instead of forcing a full rewrite.
A strong pytest specialist should understand fixtures, parametrization, markers, mocking, and coverage reporting. In practice, they also need solid Python knowledge, CI experience, and the ability to read production code. For async services, familiarity with async testing and event loops is a plus.
A pytest expert can start with a small slice of the codebase, but they need enough context to understand the test pyramid, the CI setup, and the failure patterns. Even without full product knowledge, a good specialist can improve structure, reduce flakiness, and add meaningful coverage. The best results come when the team shares examples of existing tests and current pain points.
Yes. pytest work is easy to review remotely because test files, fixtures, and failures are text based and fit normal Git workflows. For Berlin teams, a mix of remote delivery and occasional on-site sessions can help when a legacy suite needs close collaboration or when release deadlines are tight.
If pytest tests are slow, unstable, or hard to trust, outside help is usually worth it. Other signs are duplicated setup, unclear fixture layers, and tests that break for reasons unrelated to the code change. A good specialist can separate useful coverage from noisy checks.
Look for tests that are easy to read, stable in CI, and targeted at real risk areas. Strong pytest work should improve confidence without adding brittle mocks or oversized fixtures. Ask for examples of how the specialist simplified test setup, fixed flaky cases, or made failures easier to diagnose.
The average hourly rate of freelancers in Berlin, Germany who have used pytest in their recent projects is 83 €, which corresponds to a daily rate of about 664 € 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, 86% hold at least a Master's degree, and 14% 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 (13%).
The most common industries among freelancers in Berlin, Germany who have used pytest in their recent projects are Information Technology (100%), Education (50%), and Automotive (38%).
The most common business areas among freelancers in Berlin, Germany who have used pytest in their recent projects are Information Technology (88%), Product Development (75%), and Quality Assurance (75%).
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
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
