Stream Processing Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Stream Processing
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Alexandru G.
Last position:
Principal Cloud DevOps Architect at BP
In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.
Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.
In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.
To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.
Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.
Achievements:
- Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
- Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
- Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
- Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
- Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
- Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
- Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
- Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
- Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
- Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
- Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
- Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
- Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
- Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.
Tech stack:
- Infrastructure as Code: Terraform, AWS CDK, Ansible.
- Containers: Kubernetes on EKS, AKS, Docker.
- Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
- Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
- Backend: Python with FastAPI, also Node.js with NestJS.
- Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
- CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
- Monitoring and Observability: Prometheus and Grafana.
- Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
- ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
- Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
- Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.
Hardeep B.
Last position:
Sr. Data Engineer at Charles Schwab Bank
- Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
- Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
- Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
- Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
- Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
- Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
- Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
- Created and maintained dashboards in Power BI to provide actionable insights.
Timo K.
Last position:
Senior Fullstack Developer
- Lead Developer / Tech Lead in a large banking group for a complete redevelopment of payment systems
- Rebuilding payment processing in stream processing
- Development and design of cross-border payment stream processing based on Kafka and Kafka Streams
- Development of different input formats for the business team using Drools rules to dynamically adjust validation rules
- Evaluation and comparison of different public cloud providers regarding the databases they offer
- Technologies & Tools: Java 21, Kotlin, Spring Boot, Kafka, Kubernetes, Azure, TDD, JEE, REST, Angular, Spark, Google Cloud, SCDF, PostgreSQL, Drools, Git, S3, MongoDB
Andreas C.
Last position:
Agile Tester at EWE AG
- Test management / coordination and defect management for the decentralized feed-in process (Meter2Cash process)
- Test conception and creation of test cases, test execution and test plans
- Preliminary defect analysis and dispatching
Ahsan J.
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
Volker K.
Last position:
Head of Engineering at Infoniqa
- Led engineering execution: roadmap planning, capacity alignment, risk management, dependencies, and delivery tracking.
- Consolidated multiple payroll product lines into a unified SaaS platform on Dynamics 365 Business Central, enabling scalable post-merger operations and reducing operational complexity across the portfolio.
- Restructured engineering and product teams in a remote-first setting across Germany, Austria and Poland, consisting of five cross-functional units: compliance/enabling, platform, DevOps and two stream-aligned teams with total FTE depending on phase of reorganisation.
- Rebuilt the mid-level leadership layer and mentored engineering leaders, establishing a leadership pipeline and strengthening architectural decision-making across teams for scalable growth, delivery ownership and predictability.
- Designed platform foundations and system boundaries using Team Topologies aligned structures, enabling scalable ownership, clear interfaces and parallel development across distributed teams.
- Spearheaded AI transformation by implementing AI-assisted SDLC practices using SpecKit and GitHub Actions for automated, executable specifications, while delivering agentic product capabilities by securely exposing platform data and services to AI agents and copilots via RAG-based retrieval pipelines and MCP-style extensions.
- Drove modularisation of tightly coupled legacy logic into independently deployable services, improving maintainability, testability and architectural clarity while preserving continuity through targeted, low-risk extraction rather than full rewrites.
- Established observability, CI/CD and DevOps governance as platform capabilities, increasing automated compliance gates from 25% to 75% and improving deployment cadence by 40% across 15+ product versions.
- Improved operational resilience using DORA-aligned practices (lead time ↓50%, SaaS MTTR ↓85%), strengthening reliability and reducing support overhead.
- Coordinated engineering recovery for the German payroll platform during a company-wide P0 ransomware incident; restored platform continuity within 72h, validated data integrity, and rolled out hardened runbooks and automated recovery playbooks.
- Responsible for budget compliance and cost oversight in Engineering, with limited P&L responsibility and participating in the annual COGS/OPEX/CAPEX planning cycle.
Discover over 15,000 top freelancers
Statistics of experts using Stream Processing
Aggregated from the professional profiles of matched freelancers.
Experience
22 years
Position duration
3 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
17%
Certifications per freelancer
2
Most common languages
English, German, Spanish
Speak two or more languages
88%
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 Germany 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 Germany using Stream Processing
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.
Stream Processing 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%)
- Automotive (63%)
- Banking and Finance (63%)
- Education (50%)
- Energy (38%)
- Healthcare (38%)
- Transportation (38%)
- Construction (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Real-time data
Stream Processing handles data as it arrives instead of waiting for a batch job to finish. It turns events from applications, devices, transactions and operational systems into immediate results. Companies use it for live dashboards, fraud detection, alerts, recommendations and automated decisions.
Core architectures
A stream-processing solution connects producers, brokers, processing logic, state stores and downstream systems. Specialists choose between event-driven services, streaming analytics and hybrid designs that combine real-time and historical data. They also define event schemas, delivery guarantees, ordering rules, retention and recovery behaviour.
Ecosystem and tooling
The ecosystem includes Apache Kafka for durable event transport, Kafka Streams for application-level processing, and Apache Flink for stateful computation and complex event handling. Related work may involve Kafka Connect, Schema Registry, Apache Spark Structured Streaming, cloud-native messaging services, SQL-based streaming tools and observability stacks.
- Design topics, partitions, schemas and consumer groups
- Build filtering, enrichment, joins, windows and aggregations
- Connect streaming pipelines to databases, APIs and data lakes
- Test replay, failure recovery, backpressure and delivery guarantees
When expertise helps
Companies bring in freelance specialists when an existing pipeline cannot keep pace, produces inconsistent results or is difficult to operate. They are also valuable during migrations from batch processing, cloud adoption, platform consolidation and the launch of products that depend on immediate data. In Germany, collaboration may combine remote delivery with on-site workshops across distributed teams.
- Events are duplicated, delayed or lost without a clear cause
- Teams need real-time insight from high-volume operational data
- A Kafka or Flink environment needs a safer production design
- Streaming workloads must integrate with legacy systems
Skills that matter
Strong professionals understand distributed systems, concurrency, data modelling and failure handling. They can work across Java, Scala, Python or SQL, depending on the chosen tools, and know how to secure event flows, manage schemas and control infrastructure costs. Useful adjacent skills include Kubernetes, cloud services, API design, data warehousing and observability.
Quality in delivery
Good work is visible in clear event contracts, reproducible tests and operational runbooks. Specialists measure lag, throughput, processing time and recovery behaviour without treating dashboards as a substitute for sound design. They explain trade-offs plainly, document ownership and retention, and leave a pipeline that teams can operate after handover.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Stream Processing.
Stream Processing analyses and transforms events continuously as they are produced. Typical applications include fraud detection, real-time monitoring, recommendation systems, connected devices, logistics tracking and operational alerts.
Stream Processing works on data while it is moving, so results can be available almost immediately. Batch processing collects data and handles it at scheduled intervals, which can be simpler for historical reporting but unsuitable for time-sensitive decisions.
Apache Kafka is primarily an event streaming and durable transport platform, while Apache Flink focuses on stateful computation over streams and batches. They are often used together, although the right choice depends on processing logic, latency needs, operational skills and the existing data architecture.
A strong Stream Processing specialist should understand distributed systems, event modelling, databases and observability. Experience with Apache Kafka, Apache Flink, Kubernetes, cloud infrastructure, schema management and programming languages such as Java, Scala, Python or SQL is often useful.
The required depth depends on the workload and its consequences if data is delayed or lost. A simple pipeline may need focused implementation skills, while financial, industrial or customer-facing systems require proven judgement around state, replay, security, scaling and incident recovery.
Stream Processing projects are often suitable for remote collaboration because code, infrastructure and monitoring can be shared securely. On-site workshops can still help with domain discovery, architecture decisions and coordination across German-speaking or international teams.
Ask how the specialist handles out-of-order events, duplicates, schema changes, backpressure and service recovery. A capable Stream Processing professional can explain these trade-offs, show how they test failure scenarios and define useful operational signals such as consumer lag and processing latency.
Clarify event sources, expected freshness, data volume patterns, retention, delivery guarantees and the systems that consume the results. For Stream Processing, also agree on ownership of schemas, replay procedures, access controls, production support and documentation before implementation begins.
The average hourly rate of freelancers in Germany who have used Stream Processing in their recent projects is 93 €, which corresponds to a daily rate of about 744 € based on an 8-hour working day.
Of the freelancers in Germany who have used Stream Processing in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Germany who have used Stream Processing in their recent projects have 22 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Germany who have used Stream Processing in their recent projects are English (100%), German (88%), and Spanish (13%).
The most common industries among freelancers in Germany who have used Stream Processing in their recent projects are Information Technology (100%), Automotive (63%), and Banking and Finance (63%).
The most common business areas among freelancers in Germany who have used Stream Processing in their recent projects are Information Technology (100%), Product Development (88%), and Research and Development (63%).
Main locations of FRATCH Experts, who have recently used Stream Processing
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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