PySpark Experts in Düsseldorf
for scalable data processing, matched in minutes with vetted and available freelancersHire experts who build batch and streaming data pipelines with PySpark, Apache Spark and Python, connect cloud data platforms, and optimize distributed workloads. Get fast, precise matching with vetted, available freelancers for remote or on-site collaboration in Düsseldorf.
Meet FRATCH Experts in Düsseldorf, who have recently used PySpark
Murad K.
Last position:
Senior AI/ML Engineer at Quantistry GmbH
- Led end-to-end delivery of ML/AI applications for the QuantistryLab simulation platform, from requirements definition and solution design to development, testing, deployment, and production support, coordinating efforts across back-end, DevOps, and ML teams
- Designed and implemented a compound search system with an open-weights LLM, the Pydantic AI agent framework, and the Model Context Protocol (MCP), exposing it through FastAPI endpoints
- Deployed and configured vLLM-based LLM serving on Kubernetes
- Built CI/CD pipelines and Helm charts to automate deployments across staging and production environments
- Instrumented the search system with OpenTelemetry spans and traces, integrating with the Honeycomb observability platform to monitor component-level latency and functional errors
- Developed a custom evaluation dataset and implemented evaluations for retrieval relevance, answer correctness, tool-call accuracy, and end-to-end task success
- Reduced infrared spectrum prediction time by 83% (from 60 to 10 minutes) by replacing a quantum-mechanical model with a pretrained graph neural network; fine-tuned the model using PyTorch, built and containerized the inference pipeline with Docker, and integrated it into the platform
- Implemented an MLflow model registry with MinIO-backed artifact storage
- Advised sales and science teams on ML/AI applications for prospective client projects, providing technical expertise to shape proposed solutions and prototypes
Technologies: AWS, Hetzner, Kubernetes, Helm, Docker, vLLM, Pydantic AI, MCP, Qwen, Llama 3, MLflow, MinIO, GitHub Actions, Python, FastAPI, OpenTelemetry, Honeycomb, PyTorch, Polars, pytest
Ehsan M.
Last position:
BI Developer at Telecommunications Infrastructure
- Gathered and analyzed requirements in close collaboration with business departments to develop data-driven solutions and scalable PowerBI reporting infrastructures.
- Conducted detailed error analyses and optimized existing semantic and data models to ensure a robust data architecture and high data quality and to improve PowerBI reporting.
- Developed and implemented scalable ETL processes for data preparation and integration.
- Built a wiki for table and view descriptions as well as PowerBI PowerQuery and KPI documentation to ensure a consistent understanding of data structures and key performance indicators.
Olga M.
Last position:
Migration of the existing DWH from MS SQL Server to Azure Databricks at Oraylis GmbH
- Led the migration of the existing DWH, including HR data, application processes, project business and time tracking, from an MS SQL Server environment to Azure Databricks
- Refactored existing data models and ETL processes and extended them with new objects and functionalities
- Used Azure Databricks (Unity Catalog, Notebooks, Spark SQL, Databricks Dataflows) and Power BI
- Technologies: MS SQL Server 2019 (SSMS, SSIS), Power BI, Delta Lake, Azure Databricks
Aziz A.
Last position:
Senior Data Scientist & AI Engineer Consultant at Lialab SAS
- At Groupama: Developed multiple chatbots using Retrieval-Augmented Generation (RAG) to optimize internal processes and customer communication.
- At PwC: Set up an AI lab and developed various AI use cases.
- At La Poste: Analyzed and improved data quality in the data lake.
- At ARTE TV: Built a recommendation system using NLP for better content discovery.
Jacqueline H.
Last position:
Kitchen Appliance Manufacturer
Setting up and enhancing an international subscription management system
Migrating and decommissioning a Hybris-SAP system and integrating Zuora
Implementing numerous interfaces to various systems (external and internal)
Setting up and improving monitoring with Grafana, Prometheus, and Databricks
Introducing logging with Loki
Implementing business requirements using jQuery, Thymeleaf, Spring Boot, Java, Go, and Python
Adjusting and extending the GitLab CI pipelines
Setting up and extending infrastructure as code with Terraform and ArgoCD on AWS
Setting up and running E2E and performance tests
Expanding the Databricks business intelligence solution to provide various KPIs
Responsibilities: Architecture, DevOps, development, unit tests, coaching employees and apprentices, E2E, smoke, and integration tests, agile methods consulting (Kanban, Scrum)
Technologies and methods: Java, Go, Kotlin, Node.js, Spring Boot, SOAP/REST, Quarkus, Thymeleaf, Vaadin, Slack, Jira, Confluence, IntelliJ, GitLab, SQS, Kafka, AWS, Kubernetes, Terraform, Maven, Gradle, GitLab CI, Databricks
Sara W.
Last position:
Recruiting & Employer Branding Data Specialist at Sipgate
- Managed the entire hiring process with a focus on data-driven decision-making.
- Analyzed recruiting data using Excel and ATS to optimize processes effectively.
Discover over 15,000 top freelancers
Statistics of experts using PySpark
Aggregated from the professional profiles of matched freelancers.
Experience
11 years (Germany: 14 years)
Position duration
1.8 years (Germany: 2.8 years)
Positions per freelancer
6 (Germany: 10)
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Banking and Finance, Insurance, Information Technology
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
100% (Germany: 74%)
Certifications per freelancer
3 (Germany: 4)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 96%)
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 Düsseldorf 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 Düsseldorf using PySpark
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.
PySpark experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Banking and Finance (67%)
- Insurance (50%)
- Information Technology (50%)
- Manufacturing (50%)
- Retail (50%)
- Telecommunication (50%)
- Chemical (33%)
- Energy (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What PySpark does
PySpark is the Python API for Apache Spark, a distributed computing framework for processing large datasets across clusters. It lets teams use Python to transform, join, aggregate and analyze data without moving every workload onto a single machine. PySpark supports batch processing, streaming, machine learning and graph workloads through the wider Spark ecosystem.
Where it is used
Companies use PySpark to turn raw data into reliable, queryable outputs for analytics and operational systems.
- Build ETL and ELT pipelines for data warehouses and lakehouses
- Process event streams, logs and transaction data
- Prepare features for machine learning workflows
- Reconcile data from APIs, databases and cloud storage
- Migrate large processing jobs from single-node Python tools
Ecosystem and tooling
Strong PySpark work involves more than Python syntax. Specialists work with Apache Spark SQL, DataFrames, Structured Streaming and MLlib, plus storage formats such as Parquet and Delta Lake. They may also connect Spark with Kafka, Airflow, dbt, Hadoop, Databricks and cloud services from AWS, Azure or Google Cloud.
When companies need help
Freelance expertise is useful when a pipeline is slow, unstable or difficult to operate. It also helps during a lakehouse migration, a streaming rollout, a cloud transition or a major data quality initiative. In Düsseldorf, teams in manufacturing, logistics, retail, finance and professional services may need specialists who can work with local stakeholders while collaborating remotely across Europe.
What strong specialists deliver
Good professionals design pipelines that are easy to test, monitor and change. They understand partitioning, shuffles, joins, caching, serialization and cluster configuration, then use that knowledge to control performance and resource use. They also document data contracts, failure handling, lineage and deployment steps instead of treating a notebook as the finished product.
How to assess fit
Ask for examples of production PySpark work with comparable data volumes, source systems and delivery constraints. Review how the specialist diagnosed bottlenecks, handled late or duplicate records, tested transformations and monitored jobs. For remote or on-site work in Düsseldorf, confirm communication habits, English or German language needs, availability and experience with the team’s cloud and deployment setup.
Frequently asked questions
Curious about PySpark? Here are the answers that come up again and again.
PySpark is used to process and transform large datasets across distributed computing clusters. Companies use it for data ingestion, ETL and ELT pipelines, streaming analytics, feature preparation and scheduled reporting workflows.
PySpark distributes processing across a cluster, while pandas is mainly designed for data that fits comfortably on one machine. A specialist may use pandas for local exploration and PySpark for repeatable production workloads that require parallel execution.
PySpark provides programmatic control over complex transformations, integrations and streaming logic, while warehouse SQL is often simpler for relational analytics close to stored data. The right choice depends on data location, orchestration, latency, governance and the team’s operating model.
A strong PySpark specialist usually understands Python, SQL, data modeling and distributed systems. Useful adjacent skills include Kafka, Airflow, cloud storage, Databricks, Delta Lake, containerized deployment and observability for data pipelines.
The right level of PySpark experience depends on the workload, risk and operating environment. A small batch pipeline may need strong data engineering fundamentals, while a streaming platform or cluster migration calls for proven production work with performance tuning, reliability and deployment.
PySpark work is well suited to remote collaboration because code, jobs, documentation and monitoring are managed through shared environments. Teams in Düsseldorf should agree on working hours, access controls, communication routines and whether English, German or both are needed for stakeholder work.
Review whether the PySpark solution has clear tests, sensible partitioning, reliable error handling and useful monitoring. Ask the specialist to explain trade-offs around joins, shuffles, file layout, retries, data quality and cost rather than focusing only on whether a job completes.
PySpark may be excessive for small datasets, simple transformations or low-latency requests that a database or lightweight Python service can handle more directly. A capable specialist should explain when local processing, SQL, pandas or another distributed tool is a better fit.
The average hourly rate of freelancers in Dusseldorf, Germany who have used PySpark in their recent projects is 87 €, which corresponds to a daily rate of about 698 € based on an 8-hour working day.
Of the freelancers in Dusseldorf, Germany who have used PySpark in their recent projects, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers in Dusseldorf, Germany who have used PySpark in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Dusseldorf, Germany who have used PySpark in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Dusseldorf, Germany who have used PySpark in their recent projects are Banking and Finance (67%), Insurance (50%), and Information Technology (50%).
The most common business areas among freelancers in Dusseldorf, Germany who have used PySpark in their recent projects are Business Intelligence (100%), Information Technology (67%), and Product Development (67%).
Main locations of FRATCH Experts, who have recently used PySpark
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.
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Munich 
Cologne 
Frankfurt Request a free demo
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Philipp Thomaschewski
FRATCH CEO
