pandas Experts in Cologne
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Meet FRATCH Experts in Cologne, who have recently used pandas
Beshr Alnirabieh
Last position:
System Administrator – HealthCare IT & Data Infrastructure at Cellitinnen Hospital Association
- Integration of medical modalities (including ultrasound) into the existing IT infrastructure (DICOM, HL7) – put into operation within the planned timeframe.
- Administration and optimization of PACS systems for efficient archiving and distribution of radiology image data across multiple locations.
- Ensuring consistent data quality and seamless interoperability in data exchange between HIS, RIS, and PACS.
- Close collaboration with medical staff to analyze and digitally optimize clinical workflows.
- Requirements management and test coordination when implementing clinical requirements in complex IT structures.
Fahad Razzaq
Last position:
Data Science – Operations Optimization at Netto-marken
Project: Digitalization of Warehouse Processes | Building a Data Analytics Platform.
- Built a web-based workforce allocation system that digitized daily shift planning by matching worker expertise to operational zones, replacing manual coordination with a structured workflow adopted across the site, saving supervisors time on daily planning.
- Developed a real-time operational visibility dashboard giving supervisors a live view of task throughput and outstanding workload across warehouse zones throughout the day, helping reduce overtime and idle labour costs.
- Developed a slotting optimization solution to improve warehouse picking efficiency and reduce picking time per order, working directly with operations teams from concept through production deployment.
Technologies used: Python, Django, PostgreSQL, Pandas, NumPy, HTML, Java, JavaScript, Docker, Kubernetes, AWS, Power BI, GitHub Actions CI/CD, GitOps, Claude, OpenAI
Markus Glagla
Last position:
Full Stack Developer at REWE Digital
- A warehouse valuation system was reimplemented using Java, Spring Boot, and Camunda. The backend solution focuses on integration and batch calculations, the frontend on managing formulas and reviewing results.
- Java 21
- Spring Boot
- JPA
- Maven
- REST
- Kafka
- PostgreSQL
- DB2
- Liquibase
- Google Cloud Storage
- Keycloak
- GitLab CI/CD
- Helm
- Terragrunt
- SonarQube
- Angular
- IntelliJ
- JUnit 5
- Mockito
- Open API
Jeanne Yap
Last position:
Process Engineering Intern at Procter & Gamble
- Independently initiated and deployed automated validation workflows using Python, cutting manual processing by 58% and improving efficiency
- Developed a machine learning model for synthetic defect generation, reducing downtime and production costs; deployed locally and via Databricks and Azure AI Factory
- Utilized a small dataset of image data from the production lines and extended this dataset with training on models like cycleGAN and pix2pix
- Built and optimized the Linux-based development environment for training 3D models; maintained reproducibility via GitHub
- Presented technical insights to cross-functional teams (engineers, QA, project managers), ensuring alignment of ML solutions with operational needs
Peter Brungs
Last position:
Data Warehouse Consultant (Development and Analysis) at Atruvia AG
- Developed and enhanced ETL loading jobs with IBM DataStage and optimized SQL in an IBM DB2 environment as part of the Agree21 data migration
- Analyzed data quality and developed test procedures
- Created PowerShell scripts and documented GIT deployment processes
- Technologies: RedHat Linux, IBM DB2 with DBVisualizer, IBM InfoSphere DataStage 11.7, JIRA, TortoiseGIT, TortoiseSVN, PowerShell scripts
André Filip
Last position:
GenAI Product Owner at OW Media Solutions GmbH
- Designed and led the development of an automated short-video generation system.
- Built a scalable AWS backend using Step Functions, Lambda, S3, ECS Fargate, and DynamoDB.
- Developed video rendering with OpenCV and FFMPEG; ensured maintainable Python code.
- Supervised and mentored a Python developer and trained the client in AI workflows.
- Decreased end-to-end production time from hours to minutes.
- Created a modular, extensible architecture designed to support future AI models.
Sabrine Krichen
Last position:
Team Lead at InstaDeep
- Led a team of junior Research Engineers, providing mentorship, technical guidance, and career development support to foster their growth in deep learning and machine learning engineering.
Pappu Prasad
Last position:
Senior Cloud Consultant (AWS Services and Consulting) at devoteam GmbH
- Developed automated ETL pipelines with AWS Glue and Athena to ensure consistent data quality and governance requirements
- Implemented validation, anonymization, and encryption measures for data in compliance with GDPR
- Optimized cloud costs by introducing FinOps practices and increased transparency for business units
- Monitored performance, performed root cause analyses, and ensured adherence to SLAs
- Supported data and solution architects in building scalable data models for ML and analytics scenarios
Giovanni Spinelli Barrile
Last position:
Technical Product Manager at Logicc GmbH
Acted as the primary bridge between Legal, Engineering, and Business units to ensure zero compliance violations while maintaining product velocity.
Led the development of a GDPR-compliant AI aggregator platform, managing a roadmap that balances legal constraints with aggressive feature delivery.
Scaled the engineering team from 4 to 9 developers, establishing hiring protocols and technical onboarding processes to support rapid product iteration.
Boosted the development process by introducing structured sprint cycles and backlog refinement, resulting in a 20% reduction in feature delivery time.
Architected and prototyped agentic AI workflows with n8n and RAG pipelines on Langchain.
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.1 years (Germany: 2.7 years)
Positions per freelancer
8
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Transportation, Education
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
89% (Germany: 98%)
Master's degree or higher
56% (Germany: 80%)
Certifications per freelancer
4 (Germany: 2)
Most common languages
German, English, French
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 Cologne 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 Cologne using pandas
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
Data work
pandas is the standard Python library for structured data work. Specialists use it to read files, clean messy tables, join sources, and prepare reliable datasets for analysis or reporting. It is a core tool when teams need fast work on CSV, Excel, SQL, and API data.
Common tasks
- Data cleaning and transformation
- Grouping, merging, and reshaping DataFrames
- Time series handling and feature preparation
- Exporting data for dashboards, reports, and models
Strong professionals keep data logic clear and reproducible. They know when to use vectorized operations, how to avoid slow loops, and how to spot errors early in the pipeline.
Ecosystem
pandas sits close to NumPy, matplotlib, seaborn, scikit-learn, Jupyter, and SQL tooling. In real projects, experts often combine these tools to move from raw input to analysis-ready output. They also understand file formats, date parsing, and data quality checks.
Where it fits
Companies bring in pandas specialists for reporting, analytics, forecasting support, data prep, and migration work. In Cologne, that often means helping teams in media, logistics, retail, and industrial settings where Python data workflows need to run cleanly with local systems and teams.
Why hire experts
- Legacy scripts need refactoring into clear notebooks or modules
- Data sources no longer match and joins break
- Performance drops on larger tables or repeated runs
- Teams need better handover, tests, and documentation
Freelance specialists are useful when internal teams have the domain context but need focused help on Python data handling. They can step in for short fixes or for a larger cleanup of analysis code.
What strong specialists bring
A strong pandas professional writes code that is readable, tested, and easy to extend. They choose the right index strategy, keep transformations traceable, and make sure outputs are consistent across runs. For Cologne teams, that also means working well in English and, when needed, in mixed local collaboration settings.
Frequently asked questions
Key details about pandas, drawn from the questions we get asked most.
pandas is used to work with tabular data in Python. Companies use it to clean files, combine data sources, prepare reports, and shape data for analysis or modeling. It is especially useful when the work starts with messy spreadsheets, CSV files, or SQL extracts.
pandas focuses on labeled tables, not just arrays, so it is easier for row-and-column data tasks. NumPy is stronger for numerical array operations, while SQL is better for work that should stay in the database. In practice, many specialists use all three together.
A good pandas specialist usually knows NumPy, Jupyter, SQL, and basic data visualization tools. For production work, Python packaging, testing, and clean notebook or script structure also matter. If the project touches forecasting or machine learning, scikit-learn is a common plus.
pandas projects often need help as soon as data rules become repetitive, fragile, or hard to review. A small cleanup may only need a focused specialist, while migration work, performance issues, or team-wide standards call for deeper experience. The more critical the data output, the more important solid review habits become.
Yes, most pandas work can be handled remotely because the main deliverables are code, notebooks, and data logic. Cologne teams often mix remote collaboration with occasional on-site sessions when the work depends on local stakeholders or sensitive source systems. Clear access, sample data, and fast feedback matter more than location.
pandas is still the default choice for many Python data tasks because it is familiar, flexible, and deeply integrated with the Python ecosystem. Polars can be attractive for speed on some workloads, and Dask helps with larger distributed processing. The right choice depends on file size, team skills, and how the code will be maintained.
Look for clear data transformations, good handling of missing values, and code that is easy to test. A strong pandas specialist can explain why each merge, groupby, or reshape step is needed and can show how errors are caught early. Good documentation and reusable logic are signs of mature work.
A pandas freelancer should ask where the data comes from, how it changes, and what the final output must look like. It also helps to know whether the work is exploratory analysis, a reusable pipeline, or a one-off cleanup. The answers shape file handling, testing, and how much structure the code needs.
The average hourly rate of freelancers in Cologne, Germany who have used pandas in their recent projects is 96 €, which corresponds to a daily rate of about 767 € based on an 8-hour working day.
Of the freelancers in Cologne, Germany who have used pandas in their recent projects, 89% hold at least a Bachelor's degree and 56% hold at least a Master's degree.
On average, freelancers in Cologne, Germany who have used pandas in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Cologne, Germany who have used pandas in their recent projects are German (100%), English (100%), and French (44%).
The most common industries among freelancers in Cologne, Germany who have used pandas in their recent projects are Information Technology (89%), Transportation (67%), and Education (44%).
The most common business areas among freelancers in Cologne, Germany who have used pandas in their recent projects are Information Technology (100%), Business Intelligence (78%), and Product Development (67%).
Main locations of FRATCH Experts, who have recently used pandas
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.
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Hamburg 
Munich 
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Stuttgart 
Dusseldorf 
Essen 
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Philipp Thomaschewski
FRATCH CEO
