Amazon SNS Experts in Munich
for reliable event delivery, matched in minutes with vetted freelancersHire experts who design event-driven messaging, configure topic-based fan-out and connect Amazon SNS with AWS Lambda, SQS and application services. Get precise access to vetted, available freelancers who match your project quickly.
Meet FRATCH Experts in Munich, who have recently used Amazon SNS
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.
Serge K.
Last position:
Data Scientist & MLOps (Machine Learning Operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
Martin P.
Last position:
Business Analyst and Test Manager at ProSiebenSat.1 Tech & Services GmbH
- Analysis of affected business processes taking numerous stakeholders into account
- Interface analysis, architecture and system design
- Communicating and coordinating various subprojects and interface partners
- Creating epics and user stories, maintaining the backlog, workshops and review presentations
- Support during implementation between business departments and development
- Test management including strategy and approach definition
- Test case definition, execution and approval
- Cross-team organization of integration and acceptance tests
- Support of test environments
- Technologies and tools: Java, Angular, Kubectl, REST, AWS SNS/SQS, Kafka, S4/HANA, Bruno
Tezcan D.
Last position:
Solution Architect / Project Manager at German Football Association
- Overall responsibility for the project lifecycle from scope definition to completion
- Close collaboration with platform teams, IT leaders, and external service providers
- Application of SAFe principles and structured sprint work
- Creation of a migration roadmap with clear milestones
- Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
- Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
- Regular status reports and running knowledge transfer sessions
Jiri S.
Last position:
Quality Manager/Test Management at Noriba GmbH
- Test concept creation
- Creation of test processes
- Coordination of TC development: stress tests, functional tests, performance tests, high data rate tests, integration tests, etc.
- HW testing: FPGA, RF
- Test automation and regression tests
- Ensuring 24/7 operation of the test system
- Analysis & reporting
- Regular coordination of the test team, meetings with other stakeholders
- Communication and coordination with stakeholders and the project manager
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Christof N.
Last position:
Senior Developer at Otto GmbH
- Further development of personalized advertising spaces on the Otto web shop
- Full-stack development in a Kanban-driven team of about 15 people
- Technologies: Microservices, Kotlin, Spring, Spring Boot, Gradle, MongoDB, HTML, JS, Node, SCSS, AWS
- Development process: Kanban; continuous integration with AWS CodePipeline and GitHub Actions
Janusz M.
Last position:
IoT Edge Computing / Self-Driving-Cars at Automotive consulting company
- Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
- Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
- Responsible for webinar:
- IoT edge computing: architecture, components, resources, management
- IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
- IoT processes, connectivity, data transfer and deployment, security
- Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
- Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
- Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
- Analysis of large sensor data sets with Apache Spark, Kafka clusters
- Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
- Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
- Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))
Stephan M.
Last position:
SAP
- Consulting and development for VR usage scenarios in industrial contexts
- Digital Twin, Unreal Engine VR deployments, Multi-user networking, Cloud infrastructure
- Technologies: AWS, Google Cloud, other Cloud Services; C++; Unreal Engine 5; Android, Meta Quest
Discover over 15,000 top freelancers
Statistics of experts using Amazon SNS
Aggregated from the professional profiles of matched freelancers.
Experience
24 years (Germany: 19 years)
Position duration
2.8 years (Germany: 1.8 years)
Positions per freelancer
13
Top business areas
Information Technology, Product Development, Operations
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Quality Assurance, Business Intelligence
Bachelor's degree or higher
100% (Germany: 90%)
Master's degree or higher
88% (Germany: 59%)
Doctorate
25% (Germany: 12%)
Certifications per freelancer
3 (Germany: 2)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 99%)
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 Munich 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 Munich using Amazon SNS
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.
Amazon SNS 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 (67%)
- Banking and Finance (44%)
- Professional Services (33%)
- Retail (33%)
- Telecommunication (33%)
- Utilities (33%)
- Construction (22%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Amazon SNS does
Amazon Simple Notification Service, commonly called Amazon SNS, is a managed publish-subscribe service in AWS. It distributes messages from publishers to many subscribers through topics, supporting application events, alerts and user notifications without requiring teams to operate messaging servers.
Where it fits
SNS sits between event producers and receiving systems. A topic can fan out one message to Amazon SQS queues, AWS Lambda functions, HTTP or HTTPS endpoints, email, SMS and mobile push services. This makes it useful for decoupled systems that must react to business or infrastructure events.
Typical delivery work
- Create topics, subscriptions and message attributes
- Connect SNS with SQS for durable, asynchronous processing
- Trigger Lambda functions from application events
- Configure email, SMS, mobile push or webhook notifications
- Route messages across AWS accounts or Regions where needed
Experts also handle payload design, filtering policies, retry behavior and dead-letter queues so each subscriber receives the right event in a controlled way.
Ecosystem and tooling
Strong SNS specialists work comfortably with IAM, AWS CloudFormation, AWS CDK, Terraform and CloudWatch. They understand Amazon SQS, EventBridge, Lambda, API Gateway and common application frameworks that publish or consume events. They also use AWS SDKs, the AWS CLI and infrastructure-as-code pipelines to make configurations repeatable.
When companies need help
Companies often bring in freelance expertise when they are replacing point-to-point integrations, introducing asynchronous workflows or extending an existing AWS system. A specialist can review topic and subscription design, improve delivery observability, secure cross-account access and prepare a safe migration. Munich teams may choose on-site collaboration for workshops while keeping implementation remote across German and international teams.
What strong specialists deliver
Quality work goes beyond creating a topic. Strong professionals define clear event contracts, choose suitable delivery protocols and plan for duplicates, delays, retries and subscriber failure. They apply least-privilege IAM, encryption and filtering, then test failure paths and document operational ownership. They can explain when SNS is preferable to SQS or Amazon EventBridge and leave a maintainable setup for the next team.
Frequently asked questions
Not sure where to start with Amazon SNS? These answers cover the essentials.
Amazon SNS is used to publish one message to multiple subscribers through a topic. Companies use it for application events, fan-out to SQS queues, Lambda triggers, monitoring alerts, email, SMS, mobile push and webhook notifications.
Amazon Simple Notification Service is primarily a publisher-to-subscriber fan-out service, while Amazon SQS provides durable queues for consumers to process messages independently. They are often combined: SNS distributes an event, and separate SQS queues buffer work for each receiving system.
AWS SNS is a strong choice for straightforward topic-based fan-out and direct delivery to queues, functions or endpoints. Amazon EventBridge is usually better when teams need richer event buses, rule-based routing across sources or integrations with SaaS services.
A capable Amazon SNS specialist should also understand IAM, AWS Lambda, Amazon SQS, CloudWatch and infrastructure as code. Practical knowledge of AWS CDK, CloudFormation or Terraform helps make topics, subscriptions and permissions reproducible.
The right level depends on the scope. A simple notification flow may need focused configuration skills, while cross-account delivery, regulated data, high-volume fan-out or complex failure handling calls for a professional who has designed and operated Amazon SNS in production-like systems.
Amazon Simple Notification Service work is well suited to remote collaboration because configuration, code review, testing and observability can be handled through shared AWS environments and repositories. On-site workshops in Munich can still help with event modelling, security decisions and alignment with local product teams.
Ask the specialist to explain message flow, subscription filtering, retries, duplicates, dead-letter handling and permission boundaries. A strong Amazon SNS professional will show how they test failed deliveries, monitor outcomes and document event contracts rather than focusing only on initial setup.
A freelancer working with AWS SNS should be comfortable discussing data handling, access control and operational ownership with German teams. Clear English is often useful in international AWS environments, while German can support workshops and stakeholder communication in Munich.
The average hourly rate of freelancers in Munich, Germany who have used Amazon SNS in their recent projects is 110 €, which corresponds to a daily rate of about 882 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Amazon SNS in their recent projects, 100% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Munich, Germany who have used Amazon SNS in their recent projects have 24 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Munich, Germany who have used Amazon SNS in their recent projects are German (100%), English (100%), and French (22%).
The most common industries among freelancers in Munich, Germany who have used Amazon SNS in their recent projects are Information Technology (100%), Automotive (67%), and Banking and Finance (44%).
The most common business areas among freelancers in Munich, Germany who have used Amazon SNS in their recent projects are Information Technology (100%), Product Development (100%), and Operations (78%).
Main locations of FRATCH Experts, who have recently used Amazon SNS
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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Philipp Thomaschewski
FRATCH CEO
