Big Data Experts in Stuttgart
for scalable data products, matched with vetted freelancers in minutesHire experts who design data platforms, build Spark and Hadoop pipelines, and turn streaming or batch data into reliable analytics. FRATCH matches you quickly and precisely with vetted, available freelancers for your project.
Meet FRATCH Experts in Stuttgart, who have recently used Big Data
Will O.
Last position:
Solution Architect at Stuttgart Police Authority
Designing the migration strategy for WLS applications to the BKA cloud and Stack IT
Creating a hybrid cloud for internal testing and development with Kubernetes
Transforming the IT landscape from a legacy monolithic architecture to a cloud-based architecture
Integrating new CI/CD processes using Azure Pipelines
Quadrupled the throughput of personnel security checks
Migrated personnel security clearance applications to Docker-based applications
Built CI/deploy pipelines with Azure DevOps
Environment: Java Spring Boot, Kubernetes, Oracle WebLogic, OpenID, Camunda, JUnit, TOGAF
Albert F.
Last position:
Lead Product Owner at CMBlu Energy AG
- Lead Product Owner for 4 development teams
- Leading and coordinating a greenfield project with parallel implementation of core components by independent teams; managing dependencies and resources
- Establishing a data lakehouse approach, including analysis of data volumes and future requirements as part of a cloud migration (best-of-breed approach)
- Responsible for requirements analysis, selection, and piloting of a LIMS/ELN system, supported by advising decision-makers and managing external vendors
- Introducing and managing an OpenWeb UI and Azure OpenAI-based RAG system to support knowledge extraction and data-driven analyses
- Setting up, configuring, and managing Jira projects, as well as developing project-specific workflows and automations
- Implementing classic Scrum processes with all ceremonies and taking on the Scrum Master role for all involved teams
- Assisting in hiring through interviews and assessments from a product owner's perspective
- Making key architectural decisions, including selecting the platform for the data lakehouse (Databricks) and the strategic integration of LIMS and analytics platforms
Lothar H.
Last position:
Solution Manager for PoC investigation and for replacing and refining an existing cloud and IoT power plant control system at Shell AG and NEXT Kraftwerke GmbH
- Investigating the PoC and replacing and refining an existing cloud and IoT power plant control solution for certificate management of renewable energy plants (solar, wind, biomass, etc.) for the largest European renewable energy network operator, including examining the field IoT devices and their integration and network connections, aiming for automated PKI key management.
- Messaging with Kubernetes for large API-connected enterprise applications
- Risk and attack vector analysis, software quality & bug assessments
- Agile workload scheduling via Jira & Confluence
- Evaluation of pub/sub message streams for MQ and Kafka.
- Various tests with “best design approaches” for resilience patterns like circuit breaker designs, throughput measurement and monitoring, bulkhead governors – for robust EA design
- Integration of API management with IBM's API Connect to Kubernetes for platform-agnostic microservices API management designs. Google Apigee
- Solution health check approaches for load burst situations + remedy
- Research and evaluation of potential sub-service providers in the coordinating role as solution manager. CIS20 controls.
- DREAD and STRIDE security assessments.
Thomas A.
Last position:
Interim Management at Vincorion Power Systems GmbH
- Process and project management to optimize products and development processes for energy systems
- Technical risk management
- Requirements engineering and system architecture
- System FMEA of power generator units and energy storage modules aiming for generic structures
- Claims management according to Section 313 of the German Civil Code
- Regulatory environment: military and NATO standards, AQAP, VG norms
Dennis D.
Last position:
Founder at Latence
- Founded Latence to commercialise runtime safety patterns from HALO as a deployable product.
- Built end-to-end as single technical founder with open-source stack on NVIDIA ecosystem.
- Developed TRACE: real-time safety layer for knowledge agents and RAG pipelines with groundedness scoring, prompt-attack detection, GDPR redaction, context compression, audit-ready traces.
- Developed vLLM Factory: production inference framework on vLLM with custom Triton kernels and 12 parity-validated plugin models, achieving up to 11.7Ă— throughput vs vanilla PyTorch.
- Developed ColSearch: single-node multi-vector late-interaction retrieval engine with Rust SIMD and fused CUDA, achieving 3.12Ă— FastPlaid geomean QPS on BEIR-8 and a 1.58-bit quantized lane 6.4Ă— smaller than FP16.
- Developed llm-opt: LLM compression research framework with hierarchical importance, structured pruning, tabu search, knowledge distillation.
Dean R.
Last position:
CEO / Chief Scientist at ENUM
- Blockchain platform technology
- Blockchain digital platform / Digital Economy.
Harshal V.
Last position:
Project Intern at Global Shapers Stuttgart Hub
- Part of sustainability workshops and youth development sessions (“Purpose Factory”).
Tom D.
Last position:
Co-founder & CTO at Ferris Labs AG
- Focused on talent and product development as well as solution architecture
Joachim K.
Last position:
Incubator at DatenBerg GmbH
Discover over 15,000 top freelancers
Statistics of experts using Big Data
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
3.3 years (Germany: 3 years)
Positions per freelancer
14 (Germany: 12)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Professional Services
Certification focus areas
Information Technology, Operations, Product Development
Bachelor's degree or higher
86% (Germany: 92%)
Master's degree or higher
86% (Germany: 65%)
Doctorate
14% (Germany: 17%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 98%)
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 Stuttgart 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 Stuttgart using Big Data
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.
Big Data 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 (89%)
- Automotive (78%)
- Professional Services (67%)
- Government and Administration (67%)
- Manufacturing (56%)
- Education (44%)
- Energy (44%)
- Transportation (44%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data at scale
Big Data covers the methods and systems used to collect, store, process and analyze datasets that exceed the practical limits of conventional databases. It supports high-volume event streams, large analytical workloads and complex data products. Specialists turn raw information into dependable data that teams can use.
What it builds
Common deliverables include:
- Batch and streaming pipelines for operational and analytical data
- Lakehouse and data lake architectures for structured and unstructured sources
- Real-time dashboards, reporting layers and machine learning datasets
- Data quality, lineage and governance processes
These systems support connected products, logistics, manufacturing, finance, retail and scientific workloads.
Ecosystem and tooling
A Big Data environment can combine Apache Spark, Apache Hadoop, Kafka, Flink, Hive, Airflow and cloud storage. Strong specialists understand distributed processing, partitioning, schemas, orchestration and resource management. They also work with SQL, Python or Scala, containers, cloud services and observability tools.
When expertise matters
Companies bring in freelance professionals when data volumes grow, pipelines become unreliable or a new platform must be introduced without disrupting operations. In Stuttgart, this can be relevant for industrial, mobility, engineering and research teams handling sensor, production or supply-chain data. Remote delivery works well when documentation, access and ownership are clearly organized; on-site collaboration can help with complex operational environments.
Signs to seek support
Bring in specialist expertise when:
- Batch jobs miss processing windows or fail without clear diagnosis
- Streaming data arrives late, duplicated or out of order
- Cloud and on-premise systems lack a consistent data architecture
- Analysts cannot trace metrics back to trusted sources
A professional should connect technical decisions to data consumers, operating costs, security needs and measurable reliability.
What strong specialists deliver
The best Big Data professionals start with data contracts, workload patterns and failure scenarios rather than selecting tools first. They create maintainable pipelines, automate testing and monitoring, and make recovery paths explicit. They communicate trade-offs clearly and leave behind useful documentation, reproducible deployments and a platform the internal team can operate.
Frequently asked questions
Key details about Big Data, drawn from the questions we get asked most.
Big Data is used to collect and process large, fast-moving or varied datasets. Companies use it for operational analytics, recommendation systems, fraud detection, predictive maintenance, customer insights and machine learning.
Big Data platforms are designed for distributed processing across large or rapidly changing datasets. A traditional warehouse often focuses on structured, modeled data and governed reporting, while modern lakehouse designs can combine both approaches.
A strong Big Data specialist may work with Apache Spark, Kafka, Flink, Hadoop, Airflow, cloud storage and SQL. Useful adjacent skills include Python or Scala, data modeling, Kubernetes, infrastructure automation, security and observability.
The right level depends on the workload, risk and existing platform. A focused pipeline improvement may need a specialist who can work within an established stack, while a new distributed platform requires experience with architecture, migration, operations and governance.
Yes, many Big Data tasks can be completed remotely when secure access, clear interfaces and shared documentation are available. On-site work may be useful for factory systems, sensitive infrastructure or workshops with local data owners.
Spark is often preferred for fast batch analytics, iterative processing and unified workloads across batch and streaming data. Hadoop remains relevant in established environments, particularly where its storage and resource-management components are already embedded.
Ask how the specialist handles schema changes, late data, retries, backfills, partitioning and data quality. A capable Big Data professional can explain trade-offs in plain language and show how pipelines are tested, monitored, secured and recovered.
A Big Data freelancer should clarify data sources, volumes, latency expectations, retention, access controls, ownership and the current toolchain. They should also understand who consumes the outputs, how incidents are handled and whether remote collaboration or on-site work is expected in Stuttgart.
The average hourly rate of freelancers in Stuttgart, Germany who have used Big Data in their recent projects is 108 €, which corresponds to a daily rate of about 866 € based on an 8-hour working day.
Of the freelancers in Stuttgart, Germany who have used Big Data in their recent projects, 86% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Stuttgart, Germany who have used Big Data in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 3.3 years.
The most common languages among freelancers in Stuttgart, Germany who have used Big Data in their recent projects are German (100%), English (100%), and Spanish (11%).
The most common industries among freelancers in Stuttgart, Germany who have used Big Data in their recent projects are Information Technology (89%), Automotive (78%), and Professional Services (67%).
The most common business areas among freelancers in Stuttgart, Germany who have used Big Data in their recent projects are Information Technology (100%), Product Development (89%), and Business Intelligence (78%).
Main locations of FRATCH Experts, who have recently used Big Data
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 Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
