pandas Expert in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used pandas
Patrick L.
Last position:
Senior GenAI Fullstack Developer at SBH (Schulbau Hamburg)
Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.
- Development and implementation of an open-source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self-hosted solutions
- Development of reusable agentic workflows and business applications that enable non-technical employees to solve business problems independently
- Implementation of nine business applications with Single Sign-On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
Techstack: Python, Streamlit, Anthropic SDK (Claude), Azure, Linux, PostgreSQL, MS SQL, Angular
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Felix T.
Last position:
Data Engineering and Power BI at Wesernetz Bremen
Independently built a data platform with cross-system process monitoring.
- Designed the Bronze/Silver/Gold architecture and Python-based pipelines for diverse operational sources.
- Cleaned, harmonized, and validated data; processed and stored it in Parquet format.
- Tracked data history and built business-focused fact and dimension models for traceable reporting.
- Automated data delivery with Python and Power Automate (desktop and cloud); used SharePoint for archiving and data access.
- Developed the Power BI solution from data preparation through to visualizing process statuses, work progress, and deviations.
Technologies: Python, SQL, Parquet, Power BI, Power Query, Power Automate, SharePoint, Copilot
Torsten F.
Last position:
Data Analyst, Requirements Manager at Isabellenhütte Heusler GmbH
Analysis of the existing reporting platform, including processes and governance topics, with stakeholders from Sales and Marketing.
Detailed analysis and evaluation of customer-defined requirements for existing reporting and new dashboards to be established.
Support for stakeholders in managing sales processes and identifying KPI trends at an early stage.
Use of Microsoft Power BI as the central analysis and reporting platform.
Development of a proposal for the required further development of processes and the Power BI platform.
Documentation of AS-IS business processes and derivation of company-wide KPIs, including coordination with stakeholders.
Analysis and assessment of the customer's Power BI platform.
Analysis of processes and data governance.
Documentation of AS-IS business processes.
Development of recommendations for further developing processes and the reporting platform.
Design and implementation of dashboards in Power BI.
Development of company-wide KPIs and coordination with stakeholders.
Microsoft Power BI.
Data analytics.
KPI definition and reporting.
Dashboard design and data visualization.
Stakeholder management and requirements management.
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Khaled T.
Last position:
Consultant / DevOps Engineer at Dr. Ing. h.c. F. Porsche Aktiengesellschaft
- Porsche ID is a unified digital identity platform providing secure authentication and seamless access across Porsche’s online services, mobile apps, and connected vehicle features
- Designed and implemented new authentication and authorization functionalities for both users and systems
- Ensured high availability, security, and performance to deliver a flawless digital experience for Porsche customers
- Technologies: Auth0, Angular, Tailwind, AWS, Terraform, Github
- Methodologies: Scrum and SAFe
Ulm P.
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Polina S.
Last position:
Data Migration Lead – Process Automation, Data Engineering & Reporting at Large Public-Sector Bank
Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Alona L.
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Ahsan J.
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
Tan P.
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.
Olusina F.
Last position:
Cyber Job Simulation at Deloitte Australia
- Completed a job simulation involving reading web activity logs.
- Supported a client in a cybersecurity breach.
- Answered questions to identify suspicious user activity.
Roman K.
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Yevgeniy Ö.
Last position:
Tester, Test & Data Analyst at NORD/LB
- Analyze system requirements and mapping concepts (ETL requirements) for data flows and transformation logic in the bank's DWH
- Analyze data in DB tables and views of the DWH using SQL (DB2)
- Independently define, create, and execute test cases in JIRA Xray (SIT)
- Write SQL queries in DB2 to verify data scenarios and mappings
- Create test plans for SAP FSDP and concurrent projects
- Conduct error and root cause analyses in coordination with business analysts, developers, test managers, and the infrastructure team
- Thoroughly document test results in JIRA Xray
- Coordinate between business analysis, development, DB infrastructure, business units, and external vendors
Kevin M.
Last position:
Freelance Lecturer in Coaching at DSI Education GmbH
- Practice-oriented coaching on core aspects of data science
- Teaching advanced concepts in Python as well as automation (with Make and n8n) and ETL processes with Apache Airflow
- Weekly preparation and delivery of practice-oriented programming courses using real-world examples
- Promoting practical programming skills among participants through interactive exercises and individual support
- Developing didactic materials and adapting content to participants' skill levels
- Close collaboration with the team for continuous improvement of course quality and learning outcomes
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 12 years)
Position duration
1.8 years (Germany: 2.7 years)
Positions per freelancer
12 (Germany: 8)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
75% (Germany: 80%)
Doctorate
19% (Germany: 17%)
Certifications per freelancer
6 (Germany: 2)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 99%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Frankfurt 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 in Frankfurt 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
pandas 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 (79%)
- Banking and Finance (58%)
- Professional Services (42%)
- Retail (42%)
- Education (37%)
- Transportation (37%)
- Media and Entertainment (32%)
- Automotive (26%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
DataFrames and analysis
pandas is an open-source Python library for working with structured and time-series data. Its DataFrame and Series objects support filtering, joining, reshaping, grouping and statistical analysis. Companies use it to turn files, database extracts and API responses into reliable datasets for decisions, reports and models.
Core capabilities
Strong pandas specialists handle the full path from raw input to validated output. Typical work includes:
- Importing CSV, Excel, JSON, Parquet and SQL data
- Cleaning missing, duplicated and inconsistent values
- Joining tables and reshaping data for analysis
- Creating repeatable transformations and quality checks
- Exporting datasets for dashboards, models and operations
Python ecosystem
pandas works closely with NumPy for numerical operations and integrates with Matplotlib, Seaborn and Plotly for visual analysis. Specialists often use Jupyter Notebook for exploration, SQL for source queries and scikit-learn for machine learning preparation. PyArrow and Parquet become important when columnar storage and efficient data exchange matter.
Project fit
Companies bring in freelance pandas expertise when a reporting process relies on fragile spreadsheets, data sources do not align or an analytics team needs a dependable preparation layer. In Frankfurt, specialists may support finance, logistics, manufacturing, research and software businesses. Remote work is common, while on-site collaboration can help with workshops and access to domain knowledge.
Delivery standards
A good implementation is readable, testable and easy for another professional to maintain. It should document assumptions, preserve data types, handle edge cases and avoid silent changes to records. Experienced specialists also consider memory use, execution time, reproducibility and the point at which pandas should be combined with SQL, Polars or distributed processing.
Choosing expertise
Look for evidence of production data work rather than notebook-only experimentation. Useful signals include clear validation rules, version-controlled pipelines, tested transformations and experience with the formats and systems in your environment. Discuss sample data, privacy constraints, expected outputs and handover requirements before work begins.
Frequently asked questions
The facts hiring teams ask for most often when it comes to pandas.
pandas is used to load, clean, combine and analyze structured data in Python. Companies use it for reporting pipelines, financial analysis, operational dashboards, forecasting preparation and dataset creation for machine learning.
pandas provides labeled tables and rich operations for joins, grouping, reshaping and time-based data. NumPy is more focused on numerical arrays, while Polars can offer a faster and more memory-efficient approach for some workloads. pandas is usually more suitable than spreadsheets when transformations must be repeatable and reviewable.
A strong pandas specialist often works comfortably with Python, SQL, NumPy and data visualization libraries. Experience with Jupyter, Git, testing, APIs, cloud storage and formats such as Parquet can also be important, depending on the project.
The right level depends on data volume, source quality and delivery risk. A straightforward transformation may need focused support, while production pipelines require a pandas professional who understands testing, performance, data contracts and operational handover.
pandas projects are often well suited to remote collaboration because code, notebooks and datasets can be shared through controlled repositories and environments. On-site sessions in Frankfurt can still help when specialists need to map business processes, clarify data ownership or work closely with local teams.
pandas may become less suitable when datasets exceed available memory, transformations must run across many machines or low-latency processing is essential. A specialist may recommend SQL, Polars, Dask or a warehouse-native approach, while retaining pandas for exploration or smaller preparation tasks.
Ask how the pandas implementation validates inputs, handles missing values and prevents silent data changes. Review whether transformations are tested, documented and reproducible, and check that the professional can explain trade-offs using your actual data flows.
A pandas freelancer may deliver cleaned datasets, reusable Python modules, notebooks, automated pipeline steps, validation checks and documentation. The exact handover should define source connections, assumptions, output formats and how the workflow will be maintained.
The average hourly rate of freelancers in Frankfurt, Germany who have used pandas in their recent projects is 94 €, which corresponds to a daily rate of about 754 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used pandas in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 19% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used pandas in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Frankfurt, Germany who have used pandas in their recent projects are German (100%), English (95%), and French (21%).
The most common industries among freelancers in Frankfurt, Germany who have used pandas in their recent projects are Information Technology (79%), Banking and Finance (58%), and Professional Services (42%).
The most common business areas among freelancers in Frankfurt, Germany who have used pandas in their recent projects are Information Technology (100%), Business Intelligence (89%), and Product Development (84%).
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.
Berlin 
Hamburg 
Munich 
Cologne 
Stuttgart 
Dusseldorf 
Essen 
Nuremberg Request a free demo
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