Polars Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Polars
Volker Haase
Last position:
Data Analyst at Optaro GmbH
Creation of workflows for generating the data basis for the article import of a web shop: combining data from several sources, analyzing the requirements, designing the process with Jupyter Notebooks and Knime. Also creating code for automation in Python using Polars and Pandas.
Processing the source data, filtering and merging the source files and creating the needed intermediate products, creating the upload files, plausibility checks, quality checks.
Technologies used: PyCharm, Python, Jupyter Notebooks, SQL, Knime. Pandas, Polars
Tan Pham
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Asad Karim
Last position:
Senior AI Developer at Neuland.ai AG
- Architected and deployed a production-scale GraphRAG system using Neo4j, embeddings, and multi-hop reasoning over 120M+ nodes, improving answer precision by 32%, reducing hallucinations by 41%, and lowering retrieval latency by 38%.
- Designed and implemented an enterprise agent ecosystem using Model Context Protocol (MCP), exposing internal APIs, databases, and services as secure callable tools for autonomous workflows and system integration.
- Designed and deployed a production LLM-based email routing agent using Microsoft Graph API, MCP, and Azure OpenAI, achieving 96% routing accuracy, reducing manual triage workload by 65%, and decreasing response times from 18 hours to under 4 hours.
- Implemented autonomous agent self-correction pipelines using iterative feedback loops (Ralph Wiggum), enabling reliable error detection, automated remediation, and production-safe execution.
- Developed a multimodal semantic search platform using multimodal LLMs and vector embeddings, enabling semantic discovery across 250k+ image and video assets and improving search recall by 48%.
Gernot Lang
Last position:
Founder and Managing Director at Softwerk/Ruhr GmbH
- Architecting and developing SaaS platform for graphical definition and execution of pandas data processing pipelines
- Developed POlyglott, an open-source Python CLI tool for translation workflow management featuring PO file parsing, quality linting with glossary enforcement, and DeepL API integration for machine translation
- Developed web application for material compliance management (EU REACH) using Django and modern web technologies
- Built automated infrastructure platform using Proxmox, Terraform, and Ansible — VM provisioning, configuration management, internal DNS, and fleet-wide security hardening across multiple subnets
Sebastian Dirndorfer
Last position:
Data Scientist at CLADE GmbH
- Designed and implemented a robust Python-based data processing framework that supported the transition from R to Python and significantly improved data science productivity by providing maintainable, standardized modules for frequently used workflows, following coding best practices and DevOps principles
- Evaluated, trained, and deployed machine learning models on cloud platforms and edge devices, enabling fully automated mid-infrared (MIR) data evaluation pipelines that eliminated manual analysis steps and significantly shortened the time from measurement to prediction for customers and internal stakeholders
- Analyzed and interpreted multivariate MIR spectral data from the company’s proprietary analyzer using R and Python, supporting reliable identification and quantitation of chemical compounds in solution
Moritz Kath
Last position:
Senior DevOps Engineer GCP at tedi GmbH & Co. KG
- Design and implementation of DevOps and CI/CD practices for data and analytics teams
- Introduction of infrastructure as code with Terraform (IaC)
- Setup and maintenance of GCP user and permission management with Terraform in multi-project environment
- Design and implementation of CI/CD pipelines with GitHub
- Leading and training developer team for the introduction of IaC and CI/CD practices
- Building and optimising database connectors with Apache Arrow for terabyte scale data extraction (Oracle, SAP)
- Optimising data lake storage and warehouse ingest
Ahmed Mustafa
Last position:
Data Scientist at Fraunhofer Institute for Building Physics IBP
- Applied pose estimation frameworks on thermal images using infrared cameras to enhance temperature analysis and thermal comfort evaluation.
- Performed CFD simulations to analyze and visualize airflow and temperature distribution in enclosed spaces, providing data-driven insights for improving HVAC system efficiency.
- Used transfer learning to adapt pre-trained deep learning models for different tasks and datasets, improving accuracy and reducing training time.
- Handled large datasets and applied visualization techniques such as boxplots, scatter plots, and other graphical tools to identify trends, detect anomalies, and validate data accuracy.
- Built machine learning predictive models such as linear regression, logistic regression, and classification models.
- Containerized ML models and data pipelines with Docker and orchestrated scalable training and inference workflows using Kubernetes.
Christian Richter
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Bhavin Moriya
Last position:
Research Assistant at Hochschule Esslingen
- Working on the AnoMoB project, applying homomorphic encryption to extract insights from encrypted mobility data.
- Explored CKKS and TFHE schemes, multi-party computation, oblivious transfer, and proxy re-encryption.
- Implemented encrypted comparison and homomorphic operations on complex numbers using CKKS.
- Homomorphic encryption with OpenFHE & TFHE-rs (Rust), SQL analysis, ML (Pandas/Polars/Scikit-learn)
Santiago Buitrago Hernandez
Last position:
Personal project at Offline AI Assistant
- AI assistant using an offline model, capable of voice recognition and real time Text-to-Speech.
Discover over 15,000 top freelancers
Statistics of experts using Polars
Aggregated from the professional profiles of matched freelancers.
Experience
21 years
Position duration
1.9 years
Positions per freelancer
12
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
90%
Master's degree or higher
70%
Doctorate
20%
Certifications per freelancer
4
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Germany 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 Germany using Polars
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
Fast tabular work
Polars is a DataFrame library for fast, memory-efficient data work in Python and Rust. Companies use it to transform datasets, join tables, filter records, and prepare analytics outputs without the overhead of heavier tools. It is a strong fit when pandas code starts to feel slow or hard to scale.
Where it fits
- Data cleaning and reshaping
- CSV, Parquet, and Arrow workflows
- Feature preparation for analytics and ML
- Reporting pipelines and batch jobs
It is often chosen for short, readable pipelines that still need strong performance. Teams also use Polars in notebooks, backend services, and scheduled jobs that move data between storage layers.
Ecosystem skills
Strong specialists know the Python API and the Rust API, plus Apache Arrow concepts that explain how Polars handles memory and columnar data. They also work well with DuckDB, pandas, Spark exports, and cloud storage such as S3. This helps them fit Polars into real data stacks instead of isolated scripts.
When to bring help
Companies usually bring in freelance Polars experts when existing pandas code is too slow, when data prep needs to be rewritten, or when a new pipeline must stay simple and maintainable. In Germany, this often comes up in analytics teams, product data work, and internal reporting projects that need remote support in English or German.
What good specialists deliver
A strong professional writes clear lazy queries, uses expressions well, and avoids unnecessary materialization. They profile bottlenecks, choose the right joins and file formats, and leave code that other specialists can extend. Good delivery also includes tests, documentation, and practical handover notes.
Signals of fit
Look for experience with large CSV and Parquet files, schema handling, joins, window functions, and reliable data validation. Good Polars experts can explain when to use eager or lazy execution and when another tool is better. They should also be comfortable reviewing notebooks, scripts, and production pipelines.
Frequently asked questions
Need clarity? These are the questions we hear most often about Polars.
Polars is used for fast table-shaped data work: cleaning files, joining datasets, shaping analytics outputs, and preparing features for downstream tools. It is especially useful when Python teams need something lighter and faster than pandas for larger or more demanding workloads.
Polars and pandas both work with tabular data, but Polars is designed around columnar execution and a query style that can be faster on large workloads. pandas is still common for general analysis, but Polars is often preferred when performance, memory use, and lazy execution matter.
A Polars specialist helps when existing data code is slow, messy, or difficult to maintain. That is common in ETL refactors, reporting pipelines, notebook cleanup, and projects where CSV or Parquet processing has become a bottleneck.
A strong Polars professional usually knows Python well and understands Apache Arrow, Parquet, CSV parsing, and database-style joins. Depending on the project, experience with Rust, DuckDB, cloud storage, and data testing also adds real value.
Polars has both a Python API and a Rust API, so it fits teams from either side. Many projects start in Python for analytics or product data work, while Rust is useful when a service or library needs tighter control and performance.
Ask which file formats, data sizes, and pipeline steps the Polars expert has handled before. You also want to know how they approach lazy queries, schema changes, and testing, because those details affect whether the code will stay stable after handover.
Yes, Polars work is often done remotely because most tasks are code, data, and review driven. For teams in Germany, clear communication matters more than location, though some companies still prefer a specialist who can work in both English and German.
A good Polars expert writes concise pipelines, explains why a query is structured a certain way, and can point out where eager execution or lazy execution is appropriate. Quality also shows up in clean handover, test coverage, and a practical understanding of when another tool is the better choice.
The average hourly rate of freelancers in Germany who have used Polars in their recent projects is 89 €, which corresponds to a daily rate of about 708 € based on an 8-hour working day.
Of the freelancers in Germany who have used Polars in their recent projects, 90% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Germany who have used Polars in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Germany who have used Polars in their recent projects are German (100%), English (100%), and Spanish (20%).
The most common industries among freelancers in Germany who have used Polars in their recent projects are Information Technology (80%), Automotive (40%), and Education (30%).
The most common business areas among freelancers in Germany who have used Polars in their recent projects are Information Technology (90%), Product Development (80%), and Business Intelligence (70%).
Main locations of FRATCH Experts, who have recently used Polars
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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
