Dask Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Dask
Thomas Hoefkens
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
Sejal Vaidya
Last position:
Data & ML Engineering at Consulting
- Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
- Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
- Exploring Agentic AI & LLM-based tooling for production readiness patterns
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
Aravind Sasi Nair Purayath
Last position:
AI – Data Specialist at Emirates Islamic Bank
- Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
- Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
- Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
- Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
- Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
- Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
- Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
- Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
- Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
- Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
- Integrated testing and CI/CD workflows for robust data pipeline deployment.
Holger Dettmar
Last position:
Software Developer
- Gained familiarity with complex legacy software for controlling central ship systems (Ada, Java, C++).
- Implemented tests to identify memory leaks.
- Refactored existing project content and tests to object-oriented standards.
- Identified and fixed bugs in existing distributed Java and C++ applications on a DONAR/CORBA network.
- Products: Squish, NetBeans, MKS Integrity, DONAR, CORBA, DOORS, Windchill.
- Skills: Python, Java, Linux, C++, Ada.
Daniel Carton
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Mohamed Saleh
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Johannes Riesterer
Last position:
Managing Partner & Technical Director at Studio Fluffy UG
- Scientific Computing
- Machine Learning
- Generative Design & UX
Discover over 15,000 top freelancers
Statistics of experts using Dask
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.8 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Education, Energy
Bachelor's degree or higher
100%
Master's degree or higher
71%
Doctorate
29%
Certifications per freelancer
2
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 Dask
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
Distributed Python
Dask is a Python framework for parallel and distributed computing. It helps teams split large workloads into smaller tasks and run them across one machine or a cluster. Companies use it for data processing, analytics, and workloads that do not fit in memory.
Typical work
- Build lazy data pipelines with Dask DataFrame and Dask Array
- Parallelize Python code with delayed tasks
- Orchestrate cluster runs with dask.distributed
- Prepare scalable notebook workflows for analysis teams
Ecosystem fit
Strong specialists know how Dask fits with pandas, NumPy, SciPy, Jupyter, and object storage. They also understand how to balance task overhead, memory use, serialization, and scheduling. That is what keeps distributed Python code stable under real load.
When companies hire
Teams bring in freelance experts when pandas jobs become too slow, when ETL needs to scale, or when local scripts must move into a cluster setup. In Germany, this often comes up in analytics teams, manufacturing, logistics, and research environments that already rely on Python. Remote collaboration works well if the data access and cluster setup are clear.
What strong experts do
Strong Dask professionals read code and data flow together. They know when to use lazy computation, when to persist intermediate results, and when a different approach is better. They write clear pipelines, avoid memory spikes, and keep the system easy to maintain.
Common project signals
- Pandas workflows are timing out or using too much memory
- Batch processing needs to run in parallel
- Python notebooks have grown into production data jobs
- Cluster behavior is unclear and hard to debug
- The team needs a clean path from prototype to scalable execution
Frequently asked questions
The facts hiring teams ask for most often when it comes to Dask.
Dask is used to run Python workloads in parallel and across clusters. It is a good fit for large data preparation, scalable analytics, and jobs that start as pandas or NumPy code but need more capacity. Many teams also use it to keep the same Python workflow while handling more data.
Dask extends familiar pandas-like work to larger datasets and distributed execution. Pandas is simpler for smaller, in-memory tasks, while Dask helps when the workload grows beyond one machine. A strong specialist can tell you when to scale pandas-style code and when to refactor the pipeline.
Dask is often chosen when the team is already centered on Python and wants a lighter path into parallel processing. Spark can be the better fit for very large platform setups, but Dask is often easier for data science and Python-heavy workflows. The right choice depends on the codebase, data shape, and operational needs.
A good Dask freelancer usually brings strong Python, pandas, NumPy, and distributed systems knowledge. Experience with Jupyter, cloud storage, Linux, and container-based setups is often useful too. For production work, logging, profiling, and memory tuning matter just as much as the API itself.
Dask expertise matters when a prototype has become slow, fragile, or expensive to run. It also matters when task graphs, cluster behavior, or memory pressure are hard to reason about. In those cases, a specialist can simplify the design instead of just adding more workers.
Yes, Dask projects often work well remotely if the data access, environment, and cluster setup are documented. In Germany, teams frequently mix local stakeholders with remote Python specialists, especially for analytics and data infrastructure work. On-site time is most useful when secure systems or internal environments need hands-on access.
A strong Dask specialist explains trade-offs clearly and can show how they reduced bottlenecks, not just how they used the library. Look for clean task structure, sensible partitioning, and a calm approach to debugging memory and scheduler issues. Good work also leaves behind code that the team can support.
No, Dask is the broader Python project, while dask.distributed is the distributed scheduler and cluster component many teams use in production. A freelancer should understand both the high-level data APIs and the lower-level execution model. That combination is important when a workflow moves from a notebook to a shared cluster.
The average hourly rate of freelancers in Germany who have used Dask 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 Germany who have used Dask in their recent projects, 100% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Germany who have used Dask in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Germany who have used Dask in their recent projects are German (100%), English (100%), and Spanish (50%).
The most common industries among freelancers in Germany who have used Dask in their recent projects are Information Technology (100%), Education (63%), and Energy (50%).
The most common business areas among freelancers in Germany who have used Dask in their recent projects are Information Technology (100%), Product Development (100%), and Project Management (63%).
Main locations of FRATCH Experts, who have recently used Dask
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
