Dask Experts in Germany
for scalable Python data processing, matched in minutesHire experts who scale Python analytics beyond memory limits, parallelize pandas and NumPy workloads, and operate Dask clusters for data science and machine learning. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Dask
Thomas H.
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 V.
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 K.
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 S.
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 D.
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 C.
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 S.
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 R.
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 19 Sep 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Dask 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%)
- Education (63%)
- Energy (50%)
- Transportation (50%)
- Retail (50%)
- Insurance (38%)
- Manufacturing (38%)
- Professional Services (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Dask does
Dask is a Python framework for parallel and distributed computing. It breaks large workloads into smaller tasks that can run across threads, processes or a cluster. Its familiar APIs extend pandas, NumPy and scikit-learn patterns to datasets and computations that exceed the capacity of one machine.
Core capabilities
Dask builds task graphs lazily, then schedules their execution when results are needed. Professionals use Dask DataFrame for tabular data, Dask Array for numerical workloads, Dask Bag for semi-structured collections and Dask Delayed or Futures for custom workflows. The scheduler can coordinate local development and distributed production runs.
Typical projects
- Process large parquet, CSV or database extracts without loading everything into memory
- Parallelize feature engineering and model preparation for machine learning
- Run scientific simulations and numerical analysis across a cluster
- Create reusable ETL pipelines with observable task execution
- Scale pandas and NumPy workflows while keeping Python-based interfaces
Dask fits analytics platforms, research systems, risk workflows, industrial data processing and batch machine learning. It is useful when a familiar Python workflow must handle more data or parallel work than a single process can manage.
Ecosystem and tooling
Dask works closely with pandas, NumPy, PyArrow, scikit-learn, Xarray and Jupyter. Distributed deployments may use Kubernetes, cloud compute services, YARN or Dask Gateway, with dashboards for task graphs, worker health and memory use. Strong specialists also understand Parquet, object storage, Python packaging and environment management.
When to bring in expertise
Companies often need freelance expertise when a Dask prototype is slow, unstable or difficult to move into production. Specialist support is valuable for choosing partitions, controlling memory, reducing task-graph overhead and connecting Dask to existing storage and orchestration. In Germany, projects may combine remote delivery with on-site collaboration across research, manufacturing, finance or logistics teams.
- Replace fragile single-machine processing
- Diagnose worker failures, spilling and unbalanced partitions
- Prepare a Dask cluster for repeatable operations
- Improve observability and deployment practices
What strong professionals deliver
A capable Dask professional starts with workload shape, data layout and resource limits rather than adding workers blindly. They measure scheduler overhead, partition sizes, serialization, network transfer and memory pressure, then explain trade-offs clearly. They also write tests, document execution assumptions and leave behind maintainable pipelines that teams can operate after handover.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Dask.
Dask is used to parallelize Python workloads that are too large, slow or computationally intensive for a single process. Common applications include data preparation, scientific computing, machine learning pipelines, analytics and distributed ETL.
Dask is often chosen when a team already works in Python with pandas, NumPy or scikit-learn and wants a gradual path to parallel execution. Spark can provide a broader integrated data platform, while Dask offers flexible task graphs and familiar Python APIs with less need to rewrite existing code.
A strong Dask specialist usually understands Python performance, pandas, NumPy, Parquet and cloud or on-premise storage. Experience with Kubernetes, Docker, PyArrow, scikit-learn, workflow orchestration and cluster observability is also useful.
A Dask project needs more than API knowledge when it involves distributed execution or production operations. The right professional should be able to inspect task graphs, tune partitions, manage memory and explain whether scaling Dask is preferable to changing the data model or processing approach.
Dask projects are well suited to remote collaboration because code, cluster configuration, dashboards and test data can be reviewed online. For German teams, clarify the expected language, working hours, data-access rules and whether occasional on-site workshops are needed.
Dask is a natural fit for parallel data manipulation, array computing and task graphs around the scientific Python ecosystem. Ray may be a better match for distributed services, reinforcement learning or actor-based workloads, so the choice should follow the execution model rather than brand familiarity.
Ask a Dask professional to explain partition sizing, scheduler behavior, memory management and failure handling for your workload. Review a small diagnostic exercise or architecture discussion, and look for measured improvements, clear tests and operational documentation instead of worker counts alone.
Common Dask problems include oversized or tiny partitions, excessive task-graph growth, costly serialization, data skew and workers running out of memory. Experienced specialists diagnose the workload with the dashboard and profiling tools before changing cluster capacity.
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
