pandas Expert in Frankfurt
for reliable data workflows, matched in minutes with vetted freelancersHire experts who clean and transform complex datasets, build robust analytical workflows and connect pandas with Python, SQL and Jupyter. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your project.
Meet FRATCH Experts in Frankfurt, who have recently used pandas
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 client-defined requirements for existing reporting and new dashboards.
Supporting stakeholders in managing sales processes and early detection of KPI trends.
Use of Microsoft Power BI as central analysis and reporting platform.
Developing a proposal for the necessary evolution of processes and the Power BI platform.
Gathering current business processes and defining company-wide KPIs in coordination with stakeholders.
Analysis and inventory of the client's Power BI platform.
Analysis of processes and data governance.
Recording and documenting current business processes.
Developing recommendations for process and reporting platform improvements.
Designing and implementing dashboards in Power BI.
Defining company-wide KPIs and aligning them with stakeholders.
Microsoft Power BI.
Data analytics.
KPI definition and reporting.
Dashboard design and data visualization.
Stakeholder management and requirements management.
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.
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
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.
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.
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
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
Serge K.
Last position:
Controller Specialist – IRBA / Model Validation at Alte Leipziger Bauspar AG
- Credit risk
- Quantitative methods
- Development and validation of IRBA rating systems
- Analysis and monitoring of the credit portfolio
- Reporting
- Development and validation of allowance for loan losses models (PWB)
- Support for internal and external audits
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
Jens D.
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Ahsan J.
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
Aparna V.
Last position:
Data Manager at University of Cologne
- Engineered and automated a data pipeline using GitLab CI/CD for data ingestion, validation, and loading into a central database.
- Developed Python scripts for data validation and transformation, ensuring data quality and compliance with metadata standards.
- Managed the entire data lifecycle from file-based repositories to a structured SQL Server database.
- Worked in an interdisciplinary team to establish a central database for-omics data and ensure reproducibility of computational analyses.
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
16 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, Transportation
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
85% (Germany: 80%)
Doctorate
15% (Germany: 17%)
Certifications per freelancer
5 (Germany: 2)
Most common languages
German, English, Russian
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 Frankfurt 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 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 19 Sep 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 (75%)
- Banking and Finance (56%)
- Transportation (44%)
- Retail (44%)
- Education (38%)
- Automotive (31%)
- Healthcare (31%)
- Professional Services (31%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Dataframes
pandas is an open-source Python library for working with structured data. Its DataFrame and Series objects support filtering, joining, reshaping, aggregation and time-aware analysis. Companies use it to turn raw files, database extracts and API responses into reliable datasets for analysis and reporting.
Core workflows
Professionals use pandas for exploratory analysis as well as repeatable data preparation. Typical work includes:
- Importing CSV, Excel, JSON and database data
- Cleaning missing, duplicated and inconsistent values
- Joining datasets and reshaping tables
- Grouping records for operational or financial analysis
- Producing validated outputs for dashboards and models
Ecosystem
pandas sits within the Python data ecosystem and commonly works with NumPy, Matplotlib, Seaborn, SciPy and scikit-learn. Strong workflows may also use Jupyter, SQLAlchemy, PyArrow and Parquet, while orchestration tools and cloud storage support scheduled processing. Knowledge of Python packaging, testing and version control helps turn notebooks into maintainable services.
Project fit
Companies bring in freelance pandas expertise when a reporting process is manual, datasets do not agree or an analysis needs to become repeatable. In Frankfurt, professionals may support finance, logistics, manufacturing, healthcare and research teams while collaborating remotely or with local stakeholders. They can also review existing notebooks before a larger data platform or machine learning initiative.
Deliverables
A specialist can deliver clean transformation modules, reusable notebooks, data-quality checks and documented data contracts. They may connect pandas to SQL databases, APIs, cloud files or business intelligence tools, then package the workflow for scheduled execution. Clear outputs, reproducible steps and sensible memory use matter when datasets grow beyond interactive analysis.
Quality signals
Look for professionals who explain assumptions, preserve data types and test edge cases rather than relying only on a successful notebook run. Practical evidence includes clear joins, explicit handling of missing values, profiling results and repeatable tests. For Frankfurt teams, confirm communication preferences, German or English language needs and whether secure data can be accessed remotely.
Frequently asked questions
The facts hiring teams ask for most often when it comes to pandas.
A strong pandas specialist uses the library to load, clean, transform and analyze structured data in Python. Typical deliverables include repeatable reporting workflows, prepared datasets, quality checks and inputs for statistical or machine learning models.
pandas is more repeatable and programmable than spreadsheet work, especially when transformations must run consistently across incoming files. SQL remains strong for filtering and aggregation inside a database, while pandas is useful for combining database results with files, APIs and Python-based analysis.
A capable pandas professional usually understands Python, SQL, data modeling and testing. Experience with NumPy, Jupyter, Git, cloud storage and visualization libraries is also valuable, depending on whether the work ends in a report, pipeline or machine learning workflow.
The right level depends on data volume, source complexity and the cost of errors. A simple cleanup may need focused library knowledge, while production workflows require a pandas specialist who can design tests, manage performance, document assumptions and integrate with existing systems.
Yes. pandas work is often well suited to remote collaboration through repositories, notebooks, tickets and controlled data access. Frankfurt teams should agree on access rules, review routines and whether the professional must join local workshops or communicate in German as well as English.
pandas is a strong choice for many file-based and moderate-scale analytical workflows, but it may not suit very large distributed processing jobs or strict low-latency systems. A specialist can assess whether SQL, Polars, Dask, Spark or a warehouse-first design is a better fit.
A quality pandas implementation makes data types, missing-value rules, joins and output expectations explicit. It includes tests for edge cases, readable transformations, useful documentation and a repeatable way to run the workflow outside the original notebook.
A pandas freelancer can improve a workflow by profiling inputs, reducing unnecessary copies, selecting suitable data types and separating exploration from production code. Clear benchmarks, validation checks and small reusable functions make later changes safer and easier to review.
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 752 € 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, 85% hold at least a Master's degree, and 15% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used pandas in their recent projects have 16 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 (94%), and Russian (25%).
The most common industries among freelancers in Frankfurt, Germany who have used pandas in their recent projects are Information Technology (75%), Banking and Finance (56%), and Transportation (44%).
The most common business areas among freelancers in Frankfurt, Germany who have used pandas in their recent projects are Information Technology (100%), Business Intelligence (88%), and Product Development (81%).
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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