Streamlit Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used Streamlit
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
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
Mathew D.
Last position:
Data Science Expert and AI Strategist at Freelancer
- Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
- Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
- Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
- Utilized OBS and professional audio equipment to ensure high-quality video and audio content
- Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
- Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation
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.
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
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
Discover over 15,000 top freelancers
Statistics of experts using Streamlit
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 11 years)
Position duration
1.5 years (Germany: 1.7 years)
Positions per freelancer
16 (Germany: 9)
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
80% (Germany: 79%)
Doctorate
40% (Germany: 20%)
Certifications per freelancer
9 (Germany: 2)
Most common languages
German, English, French
Speak two or more languages
100%
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 Streamlit
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.
Streamlit 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 (100%)
- Professional Services (83%)
- Banking and Finance (67%)
- Healthcare (50%)
- Media and Entertainment (50%)
- Pharmaceutical (50%)
- Government and Administration (50%)
- Advertising (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Interactive Python Data Applications
Streamlit enables teams to turn data scripts into interactive web applications without frontend overhead. Data science teams write standard Python code that executes top to bottom while the framework handles reactivity, state management, and component rendering. It serves as a rapid bridge between exploratory analysis and operational tooling.
The Snowflake and Machine Learning Ecosystem
Modern delivery revolves around Python, NumPy, Pandas, and PyTorch. As part of Snowflake, the framework integrates directly with Streamlit in Snowflake (SiS) for secure data exploration within enterprise perimeters.
- Integration with scikit-learn, Hugging Face, and LangChain
- Visualization libraries including Altair, Plotly, and PyDeck
- Enterprise caching with st.cache_data and st.cache_resource
- Secure deployment via Docker, AWS ECS, and Snowflake
Core Production Use Cases
Organizations build operational tooling to democratize complex models across departments. Typical deliverables include:
- Internal risk assessment and algorithmic scoring portals
- Generative AI prototypes and retrieval-augmented generation interfaces
- Executive analytics dashboards requiring real-time parameter tuning
- Data validation and automated annotation workflows for model training
Enterprise Demand in Frankfurt
Frankfurt hosts major financial institutions, banking hubs, logistics networks, and pharmaceutical operations. Companies in the Rhine-Main area increasingly rely on the framework to deliver internal analytical applications that comply with strict data governance policies, enabling stakeholders to audit risk models directly without exposing backend systems.
Engineering Distinction in Practice
Proficient specialists distinguish themselves by structuring clean session state workflows, managing execution lifecycles, and avoiding performance bottlenecks from full script reruns. They design modular component architectures, secure API endpoints, and establish clean multi-page application patterns that remain maintainable across large corporate environments.
Why Bring In Independent Specialists
Engaging seasoned specialists accelerates delivery from initial proof of concept to secure internal launch. External professionals establish reproducible deployment pipelines, align state caching with backend databases, and train internal data science teams on best practices for building responsive, scalable analytical tools.
Frequently asked questions
Key details about Streamlit, drawn from the questions we get asked most.
Organizations use Streamlit to build custom web applications for data science, operational analytics, and machine learning prototypes. It allows professionals to convert Python scripts into reactive tools for financial modeling, internal reporting, and model validation without requiring dedicated frontend developers.
While Dash provides fine-grained callback control and Gradio excels at quick model demos, Streamlit strikes a balance between rapid development and polished user interfaces. It delivers faster prototyping cycles than Dash and offers deeper data visualization layouts than Gradio, making it ideal for business analytics.
A qualified Streamlit specialist needs strong fluency in Python, Pandas, and SQL, along with expertise in containerization using Docker. Experience with cloud platforms such as AWS or Snowflake, authentication protocols like OAuth, and visualization tools such as Plotly is also essential.
Yes, provided the application utilizes Streamlit caching mechanisms like st.cache_data correctly to prevent unnecessary computation. Scaling also requires deploying containerized instances behind load balancers with sticky sessions enabled to handle concurrent state management reliably.
Through Streamlit in Snowflake, Streamlit applications run directly inside the data warehouse environment. This architecture allows organizations to build and distribute internal data apps without moving sensitive records across external network boundaries.
Many Streamlit specialists based in Frankfurt provide flexible arrangements, combining remote execution with on-site workshops. Local presence is particularly valuable for banking and fintech firms in the Rhine-Main area requiring in-person security reviews or architecture alignment.
Most projects around Frankfurt operate smoothly in English, especially within data science and tech teams. However, freelance specialists working with local financial institutions often benefit from German proficiency when collaborating with compliance officers and domestic business stakeholders.
Evaluate their portfolio for clean state management, modular multi-page structures, and caching efficiency within Streamlit. High-quality professionals write performant code that avoids slow UI reruns, implement secure user authentication, and follow continuous deployment standards.
The average hourly rate of freelancers in Frankfurt, Germany who have used Streamlit in their recent projects is 103 €, which corresponds to a daily rate of about 827 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Streamlit in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 40% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Streamlit in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Frankfurt, Germany who have used Streamlit in their recent projects are German (100%), English (100%), and French (17%).
The most common industries among freelancers in Frankfurt, Germany who have used Streamlit in their recent projects are Information Technology (100%), Professional Services (83%), and Banking and Finance (67%).
The most common business areas among freelancers in Frankfurt, Germany who have used Streamlit in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (100%).
Main locations of FRATCH Experts, who have recently used Streamlit
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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
