Apache Spark Experts in Dusseldorf
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Meet FRATCH Experts in Dusseldorf, who have recently used Apache Spark
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
Bernhard D.
Last position:
Enterprise Architect. And responsible for the cross-functional architecture as well as all domains with a focus on the sales at Süddeutsche Krankenversicherung
Creation of business and technical concepts as well as support for enterprise architecture management as part of the migration to the Adesso standard insurance solutions and the renewal of surrounding systems
Interface definitions
Business analysis and creation of the business concepts for input management and sales
Creation of architecture guidelines and processes
Processing technical and business decisions in the architecture board
Definition of service processes and system management aspects
Creation of the technical target operating model
Creation of an integration architecture for AI/LLMs from the providers Commasoft and Insiders
CommaSoft (Alan) webpages and web services
Insiders for Smartfix based on web services
Creation of a question catalog for a security/regulatory check.
Analysis of threats using threat modeling / STRIDE incl. creation of a set of measures to secure the systems
System environment: Jira, Confluence, Java, Enterprise Architect, VAIT/DORA, KritisV, GDPR, IT ServiceMgmt, Adesso Ecosphere and Insure Health, DoPIX and successor product, SmartFix, SmartFlow, Zabas, Kolumbus, Clarc archive, BiPRO, AI / LLM, MS Azure, AWS, TOM/FMO
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 Apache Spark
Aggregated from the professional profiles of matched freelancers.
Experience
13 years (Germany: 14 years)
Position duration
1.6 years (Germany: 2.7 years)
Positions per freelancer
8 (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: 97%)
Master's degree or higher
83% (Germany: 70%)
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 97%)
Based on our profile pool as of 5 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Dusseldorf 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 Dusseldorf using Apache Spark
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 5 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Apache Spark 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 (71%)
- Insurance (57%)
- Information Technology (57%)
- Manufacturing (57%)
- Retail (57%)
- Telecommunication (57%)
- Government and Administration (43%)
- Chemical (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed data processing
Apache Spark is an open-source engine for processing large datasets across clusters. Companies use it for batch workloads, interactive analytics, data preparation, and machine learning pipelines. Its in-memory execution and parallel processing support demanding workloads that exceed the practical limits of a single machine.
Spark workloads
Apache Spark supports data teams across the full path from raw inputs to trusted outputs:
- Batch transformation for data lakes and warehouses
- Streaming pipelines for events, telemetry, and operational data
- Feature preparation and model workflows with MLlib
- Interactive analysis through Spark SQL and notebooks
Ecosystem and tooling
The ecosystem includes PySpark for Python workloads, native Scala APIs, Spark SQL, Structured Streaming, and MLlib. Strong specialists also work with Delta Lake, Apache Iceberg, cloud object storage, Kubernetes, and orchestration tools such as Airflow. They understand connectors for databases, message brokers, and warehouse systems.
When companies hire specialists
Companies often bring in freelance Apache Spark expertise when pipelines are slow, cloud costs are rising, or a data platform must move from prototypes to reliable production use. A specialist can review partitioning, joins, serialization, cluster sizing, and failure handling. In Dusseldorf, teams may combine on-site workshops with remote delivery across distributed data and analytics groups.
Project deliverables
Typical engagements produce working pipelines, reusable transformation logic, streaming jobs, data quality checks, and deployment documentation. Specialists may also create performance baselines, monitoring dashboards, test suites, and migration plans from legacy processing tools. Clear interfaces and reproducible environments make handover easier for internal teams.
What strong professionals bring
Strong Apache Spark professionals understand distributed systems rather than treating Spark as a faster scripting tool. They can explain data partitioning, shuffle behavior, checkpointing, schema evolution, and resource management in practical terms. They also know when a simpler SQL, warehouse, or single-node approach is more suitable and communicate clearly with data, product, and operations stakeholders.
Frequently asked questions
Quick answers to the questions that come up most around Apache Spark.
Apache Spark is used to process and transform large datasets across distributed machines. Common applications include data lake pipelines, streaming analytics, exploratory analysis, feature engineering, and scheduled warehouse loads.
Apache Spark offers a broader programming model and is generally more convenient for iterative workloads, interactive queries, and streaming. Hadoop MapReduce can still suit straightforward batch processing, but Spark SQL, Structured Streaming, and in-memory execution often make complex pipelines easier to build and maintain.
A strong PySpark specialist can deliver many projects with Python, especially where data science and machine learning teams are involved. Scala knowledge is valuable for performance-sensitive applications and teams using Spark's native ecosystem, while SQL, cloud storage, and orchestration skills are useful across both approaches.
The right level depends on workload complexity, data volume, and operational risk rather than a fixed time period. A simple transformation may need focused pipeline expertise, while streaming, migration, or performance work calls for a professional who has handled cluster behavior, failures, testing, and production monitoring.
Apache Spark is well suited to remote collaboration because pipelines, notebooks, infrastructure, and tests can be reviewed in shared repositories and cloud environments. For teams in Dusseldorf, remote delivery can be combined with on-site workshops when architecture decisions or stakeholder alignment benefit from direct discussion.
Ask the specialist to explain a real pipeline design, including partitioning, joins, schema changes, retries, and monitoring. Good answers connect technical choices to reliability, cost, data quality, and maintainability instead of focusing only on writing transformations.
Apache Spark may be excessive for small datasets, simple scheduled queries, or workloads that a warehouse engine can handle directly. A careful expert compares the expected scale, latency, governance, operating effort, and team skills before introducing a distributed processing layer.
A professional should leave behind tested pipelines, deployment instructions, configuration guidance, monitoring details, and clear ownership boundaries. Depending on the scope, the deliverables may also include performance findings, data quality controls, runbooks, and a handover session for the internal team.
The average hourly rate of freelancers in Dusseldorf, Germany who have used Apache Spark in their recent projects is 90 €, which corresponds to a daily rate of about 717 € based on an 8-hour working day.
Of the freelancers in Dusseldorf, Germany who have used Apache Spark in their recent projects, 100% hold at least a Bachelor's degree and 83% hold at least a Master's degree.
On average, freelancers in Dusseldorf, Germany who have used Apache Spark in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Dusseldorf, Germany who have used Apache Spark in their recent projects are German (100%), English (100%), and French (29%).
The most common industries among freelancers in Dusseldorf, Germany who have used Apache Spark in their recent projects are Banking and Finance (71%), Insurance (57%), and Information Technology (57%).
The most common business areas among freelancers in Dusseldorf, Germany who have used Apache Spark in their recent projects are Business Intelligence (86%), Information Technology (71%), and Product Development (71%).
Main locations of FRATCH Experts, who have recently used Apache Spark
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 
Nuremberg Request a free demo
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