Amazon SQS Experts in Frankfurt
in minutes with vetted, available specialists and the power of AIHire experts who design queues, tune message flow, and build resilient async services with Amazon SQS, SQS FIFO, dead-letter queues, and AWS integrations. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Frankfurt, who have recently used Amazon SQS
Tan Pham
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Jan Mundo
Last position:
Founder, Senior Solution Architect, Team Lead, Senior DevOps Engineer at CreArt IT GmbH
- Hands-on solution architect
- Digital product development - SaaS
- Strategic consulting on software architecture, cloud migration, and DevOps processes
- Coaching SE developers and IT architects
Ashkan Zadeh
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Waleri Moretz
Last position:
Project Manager at WAMOCON Academy
- Project planning and resource planning
- Requirements definition and technology selection
- Data migration and risk management
- Monitoring up to go-live
- Tools: Office365, Jira XRAY, Strato, Onboarding App
Roman Krivtsov
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Ritika Solanki
Last position:
AWmOpsRtKekEX(CPEliRenIEtN: CInEfoSrs.yDs,aHtaitAarcchhiiEtencetr(gAyW) S)
Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.
Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.
Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.
Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.
Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.
Power BI dashboard optimization:
Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.
Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.
Collaborated with Azure data engineers to optimize data processing and publication pipelines.
Stakeholder communication & data modeling:
Acted as liaison between Group Data Office and Technology Office to align data modelling standards.
Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.
Documentation & quality assurance:
Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.
Participated in UAT sessions with business users to validate data outputs and report accuracy.
Anton Rösler
Last position:
AI-Engineer at Publicly traded company, industrial safety technology
- Designed and implemented the agent-based AI architecture for a company-wide platform to securely deploy LLM-based agents
- Designed and implemented end-to-end RAG pipelines from multiple sources: document preprocessing, chunking strategies for different document types, embeddings, retrieval with re-ranking, and robust prompt orchestration
- Developed a modular context engineering framework with skill architecture, context isolation, and dynamic resource management; human-in-the-loop control for enterprise tool integrations
- Built the CI/CD pipeline, testing strategy, tracing on the software side as well as automated LLM and agent evaluations, red team testing and tracing, and handed over to a reproducible production environment (ISO27001 and SOC2 compliant)
Doğan Can Uçar
Last position:
E-Commerce Developer at The Quality Group
Backend development for shops related to the “ESN” and “More Nutrition” shops
Technologies included: PHP, Symfony 6, Bref, AWS (SQS, Lambda), Terraform, Akeneo API
Discover over 15,000 top freelancers
Statistics of experts using Amazon SQS
Aggregated from the professional profiles of matched freelancers.
Experience
18 years (Germany: 19 years)
Position duration
1.8 years (Germany: 1.9 years)
Positions per freelancer
15 (Germany: 14)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Healthcare, Government and Administration
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 87%)
Master's degree or higher
67% (Germany: 54%)
Doctorate
17% (Germany: 9%)
Certifications per freelancer
6 (Germany: 3)
Most common languages
German, English, Russian
Speak two or more languages
100% (Germany: 95%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 Frankfurt using Amazon SQS
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Message queues
Amazon SQS is a managed message queue for decoupling services and smoothing traffic spikes. It helps teams move work between systems without tight coupling or direct calls. That makes it useful for background jobs, event-driven workflows, and resilient service-to-service communication.
Common uses
- Buffering orders, uploads, and notifications
- Feeding workers that process tasks in the background
- Breaking monoliths into smaller async services
- Handling retries with dead-letter queues
- Spreading load across busy systems
AWS fit
SQS is often used with Lambda, SNS, ECS, EKS, and Step Functions. Strong specialists know when to choose Standard queues and when FIFO ordering matters. They also understand visibility timeouts, long polling, and message retention.
What strong experts do
A good Amazon SQS specialist thinks in failure modes, not just happy paths. They design idempotent consumers, safe retries, and clear message contracts. They also watch for duplicate processing, poison messages, and queue backlogs before those problems reach production.
When to bring one in
Companies usually look for freelance expertise when queues are growing messy, workers are failing, or integrations need a cleaner design. In Frankfurt, this often fits teams working with AWS-heavy systems, internal platforms, and regulated environments that need reliable async processing. Remote collaboration works well, but on-site help can matter when several services and teams need alignment.
Delivery signals
- Clear queue naming and routing rules
- Well-defined retry and DLQ behavior
- Metrics and alarms for throughput and failures
- Consumer logic that handles duplicates safely
- Documentation that explains message shape and ownership
Frequently asked questions
The facts hiring teams ask for most often when it comes to Amazon SQS.
Amazon SQS is used to move work between systems without making them depend on each other in real time. Teams use it for background processing, event handling, buffering traffic spikes, and retrying failed tasks in a controlled way.
SQS is a queue, so it fits work distribution and decoupling between producers and consumers. SNS is better for fan-out notifications, Kafka is stronger for event streams and replay, and RabbitMQ often offers more advanced routing patterns. The right choice depends on whether you need simple buffered delivery, broadcast, or stream processing.
A strong Amazon SQS specialist should also know Lambda, SNS, IAM, CloudWatch, and the surrounding service architecture. Idempotency, retry design, dead-letter queues, and message contract design matter just as much as queue setup.
Simple queue setup is straightforward, but real projects need someone who has handled failure cases, scaling, and operational noise. If your system depends on clean retries, ordering, or many consumers, choose a specialist who has worked on production AWS workflows before.
Bring in Amazon Simple Queue Service expertise when queues keep growing, processing becomes unreliable, or teams disagree on the right async design. Outside help is also useful when you need to refactor a busy integration layer or add dead-letter handling without breaking production.
Yes. Amazon SQS work is usually easy to do remotely because most tasks are design, configuration, and code review rather than hardware-dependent work. In Frankfurt, remote specialists often fit well with AWS-based product teams, while on-site sessions help when several stakeholders need to align on messaging boundaries.
A good Amazon SQS setup has clear queue ownership, safe retry logic, a sensible dead-letter strategy, and alarms that catch backlog growth early. It also keeps consumers idempotent so duplicate messages do not create bad data or repeated side effects.
AWS SQS helps when direct calls are too fragile, too slow, or too tightly coupled. It lets one service hand off work and continue, while another service processes it when ready, which reduces cascading failures and makes peak traffic easier to absorb.
The average hourly rate of freelancers in Frankfurt, Germany who have used Amazon SQS in their recent projects is 104 €, which corresponds to a daily rate of about 832 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Amazon SQS 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 Frankfurt, Germany who have used Amazon SQS in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Frankfurt, Germany who have used Amazon SQS in their recent projects are German (100%), English (100%), and Russian (25%).
The most common industries among freelancers in Frankfurt, Germany who have used Amazon SQS in their recent projects are Information Technology (88%), Healthcare (63%), and Government and Administration (63%).
The most common business areas among freelancers in Frankfurt, Germany who have used Amazon SQS in their recent projects are Information Technology (100%), Business Intelligence (75%), and Product Development (75%).
Main locations of FRATCH Experts, who have recently used Amazon SQS
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 Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
