
Amazon EventBridge Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Amazon EventBridge
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:
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
Sara Z.
Last position:
Data Analyst / Analytics Engineer at IDG Tech Media GmbH
- Designed, built, and maintained scalable ETL/ELT data pipelines using Python, SQL, REST APIs, AWS Lambda, S3, PostgreSQL RDS, EventBridge, CloudWatch, Docker, Apache Airflow, and BigQuery – integrating data from GA4, Google Ads, Meta Ads, CMS, CRM, newsletters, events, and B2C ordering systems into analytics-ready datasets.
- Built a cross-brand lakehouse architecture from AWS to BigQuery – transforming raw JSON/CSV data into structured, partitioned, and reusable reporting layers with staging, intermediate, canonical, and mart models.
- Designed relational and dimensional data models: 3NF staging models, star schemas, fact tables, dimension tables, daily KPI aggregates, and dashboard-optimized marts for marketing, content, subscription, event, CRM, and revenue analysis.
- Implemented production-grade data quality and pipeline reliability features: incremental loads, idempotent upserts, deduplication, schema validation, row matching, null checks, anomaly detection, freshness monitoring, logging, retries, and error alerts.
- Automated cross-brand reporting processes and data products – pipelines for 73 newsletter campaigns, 31 lead list syncs, 52 event partner reports, and a 500K-record company matching pipeline; reduced manual data preparation by approx. 70% and increased analyst productivity by approx. 30%.
Christian S.
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
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
Maziyar K.
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Discover over 15,000 top freelancers
Statistics of experts using Amazon EventBridge
Aggregated from the professional profiles of matched freelancers.
Experience
21 years

Position duration
3 years

Positions per freelancer
14

Top business areas
Information Technology, Product Development, Business Intelligence

Top industries
Information Technology, Education, Retail

Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
67%
Doctorate
17%

Certifications per freelancer
2

Most common languages
German, English, Czech

Speak two or more languages
100%
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 Munich 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 Munich using Amazon EventBridge
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.
Amazon EventBridge 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 (83%)
- Education (67%)
- Retail (67%)
- Healthcare (50%)
- Media and Entertainment (50%)
- Professional Services (50%)
- Telecommunication (50%)
- Automotive (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Event routing
Amazon EventBridge moves events between AWS services, custom applications, and external SaaS tools. It is used for loose, event-driven systems where one action can trigger many others without direct service calls.
Typical work
- Define event buses and routing rules
- Connect source systems to downstream consumers
- Set up schema discovery and event contracts
- Build decoupled workflows across services
- Handle retries, dead-letter queues, and filtering
Ecosystem fit
EventBridge often sits alongside Lambda, Step Functions, SNS, SQS, and API Gateway. Strong specialists understand AWS IAM, JSON event shapes, and how to design clean event names and payloads that stay usable over time.
When to bring in help
Companies usually look for freelance expertise when integrations are brittle, event flows are hard to trace, or a new domain needs a cleaner architecture. In Munich, this often fits teams working in enterprise software, mobility, manufacturing, and connected products.
What strong specialists do
Good professionals think in contracts, failure paths, and operational clarity. They can turn business events into stable routing patterns, document producers and consumers, and avoid hidden coupling that makes systems hard to change.
Delivery focus
Amazon EventBridge work is rarely only about setup. It often includes event mapping, permission design, observability, environment separation, and cleanup of older CloudWatch Events patterns. The best specialists leave systems easier to extend, test, and support.
Frequently asked questions
Questions about Amazon EventBridge? Start with the answers below.
Amazon EventBridge is used to route events between AWS services, custom apps, and SaaS tools. It helps teams build event-driven systems where actions happen in response to business or system events instead of direct synchronous calls.
Amazon EventBridge grew out of CloudWatch Events, so many teams still use the older name when they talk about it. The current service adds broader event routing, SaaS integration, and schema features, so migration work often comes up in existing AWS environments.
Amazon EventBridge is built for event routing and filtering across multiple targets, while SNS is more about pub/sub fan-out and SQS is about durable queue-based processing. In real projects they are often used together, with EventBridge deciding what should happen next and SQS handling buffering or retries.
A strong Amazon EventBridge specialist usually knows AWS Lambda, IAM, Step Functions, SNS, SQS, and API Gateway. They should also be comfortable with JSON event design, observability, and failure handling so event flows stay clear and supportable.
A simple Amazon EventBridge setup can be handled quickly, but production work needs someone who understands routing rules, permissions, and operational edge cases. If the system spans several services or includes external SaaS sources, you want a specialist who has worked with event contracts and error paths before.
Yes. Amazon EventBridge work is usually easy to do remotely because the core tasks are design, configuration, and testing in AWS. For Munich teams, on-site time is mainly useful for architecture workshops, stakeholder alignment, or when event flows touch several internal systems.
Look for someone who can explain why a rule exists, how events are named, and what happens when delivery fails. A good Amazon EventBridge expert leaves behind clean event contracts, clear documentation, and a setup that is easy to monitor and extend.
Common Amazon EventBridge work includes connecting microservices, reacting to AWS service changes, ingesting SaaS events, and starting workflows with Step Functions or Lambda. It also shows up in modernization projects where teams replace tight integration code with a cleaner event-based design.
The average hourly rate of freelancers in Munich, Germany who have used Amazon EventBridge in their recent projects is 114 €, which corresponds to a daily rate of about 912 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Amazon EventBridge in their recent projects, 100% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Munich, Germany who have used Amazon EventBridge in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Munich, Germany who have used Amazon EventBridge in their recent projects are German (100%), English (100%), and Czech (17%).
The most common industries among freelancers in Munich, Germany who have used Amazon EventBridge in their recent projects are Information Technology (83%), Education (67%), and Retail (67%).
The most common business areas among freelancers in Munich, Germany who have used Amazon EventBridge in their recent projects are Information Technology (100%), Product Development (83%), and Business Intelligence (67%).
Main locations of FRATCH Experts, who have recently used Amazon EventBridge
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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