AWS Step Functions Experts in Berlin
matched in minutes by AIHire experts who design serverless workflows, coordinate AWS services and automate resilient business processes with Step Functions, Lambda and EventBridge. FRATCH connects you quickly with vetted, available freelancers whose skills match your project.
Meet FRATCH Experts in Berlin, who have recently used AWS Step Functions
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and maintained the product backlog
Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural
Rüdiger S.
Last position:
Full-Stack Software Engineer / Consultant for Digitalization at ARTEVENT
Designed, built, and launched an internal event planning web application used by over 100 department leads for a large event, despite having no dedicated testing phase.
Ensured smooth, failure-free operation during first production use, leading to the tool being adopted for future events.
Automated catering calculations and related workflows, significantly reducing email communication and manual computation effort for meal planning.
Managed deployment and hosting on a Linux server using Coolify, including application setup and runtime operations.
Hired and guided a communication designer on UX while independently owning all technical decisions and implementation.
Lasya M.
Last position:
Data Engineer at Carelon Global Solutions (Elevance Health)
- Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
- Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
- Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
- Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
- Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
- Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
- Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.
Jan K.
Last position:
Data Expert at Manufacturing
Deepak M.
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
Qaiser A.
Last position:
Freelance Lead DevOps Engineer at Schwarz Gruppe Produktion
Bootstrapping a CloudOps team and building a multi-cloud provider backend for a low-code Internal Developer Platform (IDP) with env zero
Introducing user story mapping, ADRs, milestones, and backlog management
Designing and developing core APIs, setting up CI/CD pipelines, OpenTofu/Terraform scripts
Representing and communicating the team with third-party stakeholders (e.g. env zero)
(Cross-)team coaching on DevOps, software design, Terraform, Golang, and agile practices
Amit G.
Last position:
Authorized Officer - Software Engineer at UBS
- Built a fee proposal tool automating financial advisor fee calculations, reducing manual processing from hours to seconds.
- Developed Spring Boot microservices with REST APIs on Azure SQL, replacing mainframe procedures, improving latency by 60%.
- Migrated Tomcat applications from on-premises RHEL servers to Azure Kubernetes Service (AKS).
- Automated testing and deployment processes using CI/CD pipelines in GitLab, reducing deployment time by 90%.
- Streamlined recurring reporting workflows by automating report generation using Spring Batch.
Muhammad A.
Last position:
Senior Software Engineer at Verbund Pflegehilfe
Own Azure-hosted ASP.NET Core microservices for chronic-care workflows, sustaining 99.8% uptime across 3,000+ patient interactions.
Shape HL7 FHIR-based patient, encounter, and observation profiles delivering a unified source of truth for clinical teams and integrations.
Implement SignalR telemetry, Application Insights alerting, and automated pipelines that surface deteriorating vitals in under three seconds.
Automated consultant outreach by orchestrating Twilio voice campaigns via n8n workflows and VAPI AI assistants, boosting successful client contacts by 55%.
Entlass Manager: Led the end-to-end build of the chronic-care coordination platform, standing up Azure-native microservices, clinical data flows, and patient-facing tooling from initial requirements through production release.
Power Dialer: Built the Twilio-driven consultancy auto-dialer and n8n workflows, raising client reachability to 85%.
Discover over 15,000 top freelancers
Statistics of experts using AWS Step Functions
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.6 years
Positions per freelancer
9
Top business areas
Information Technology, Quality Assurance, Business Intelligence
Top industries
Information Technology, Automotive, Retail
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
50%
Doctorate
17%
Certifications per freelancer
2
Most common languages
German, English, Hindi
Speak two or more languages
100%
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 Berlin 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 Berlin using AWS Step Functions
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.
AWS Step Functions 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 (88%)
- Automotive (50%)
- Retail (50%)
- Education (38%)
- Banking and Finance (38%)
- Healthcare (38%)
- Fashion (25%)
- Insurance (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Workflow orchestration
AWS Step Functions is a managed AWS service for coordinating distributed applications and business processes. Teams define workflows as state machines, then control tasks, branching, retries, parallel work and timeouts without building a separate orchestration layer. It supports Standard and Express Workflows for different execution and monitoring needs.
Core building blocks
A workflow is described with Amazon States Language, a JSON-based definition that makes execution logic explicit. Task, Choice, Parallel, Map, Wait, Pass, Succeed and Fail states cover common control patterns. Strong specialists also shape input and output with JSONPath or JSONata, use error handling carefully and keep state data purposeful.
AWS ecosystem
Step Functions commonly coordinates Lambda, Amazon ECS, Amazon EKS, Batch, Glue, SageMaker and API Gateway. EventBridge can start executions, while SQS, SNS, DynamoDB and RDS often support surrounding processes. Useful expertise includes IAM permissions, CloudFormation or AWS CDK, CloudWatch logging, X-Ray tracing, CI/CD and infrastructure testing.
Practical use cases
- Coordinate payment, approval and fulfilment processes across services
- Run ETL, data validation and machine learning pipelines
- Manage long-running workflows with retries, callbacks and human review
- Process files or records in parallel with Map states
- Orchestrate container and serverless workloads with clear failure paths
The service fits systems that need visible execution history and controlled recovery. It is less suitable when a simple event route or a tightly coupled in-process function is enough.
When expertise matters
Companies bring in freelance specialists when workflows are becoming hard to trace, when retries create duplicate actions or when integrations need consistent failure handling. They can refactor Lambda chains, select Standard or Express execution, control costs and prepare production monitoring. Berlin teams may also value professionals who can collaborate on-site or remotely across German- and English-speaking stakeholders.
Quality signals
Strong AWS Step Functions professionals model business states before writing definitions. They distinguish transient errors from business failures, make tasks idempotent and set clear timeouts, catch paths and compensation actions. They document IAM boundaries, test state transitions and use execution history, CloudWatch and X-Ray to investigate real incidents. A good delivery leaves maintainable definitions, deployment automation and runbooks for the team.
Frequently asked questions
Key details about AWS Step Functions, drawn from the questions we get asked most.
AWS Step Functions orchestrates workflows across AWS services and external systems. Companies use it for approvals, data pipelines, file processing, order fulfilment, scheduled jobs and long-running processes that need retries, branching or human interaction.
AWS Step Functions manages ordered workflow state and shows how each step completed or failed. EventBridge routes events, while SQS buffers messages between components; these services often work alongside Step Functions rather than replace it.
A strong AWS Step Functions specialist usually understands Lambda, IAM, EventBridge, SQS, DynamoDB and API Gateway. Infrastructure as code with AWS CDK or CloudFormation, CI/CD, CloudWatch and distributed-systems design are also valuable.
The right level depends on workflow risk, service integrations and operational needs, not simply on project duration. For a production process, look for someone who has designed retries, idempotent tasks, timeouts, permissions and monitoring, and can explain the trade-offs between Standard and Express Workflows.
AWS Step Functions is well suited to remote collaboration because definitions, infrastructure and execution history can be reviewed in shared repositories and AWS accounts. Berlin-based teams should agree on working hours, documentation and whether German or English is needed for workshops and operational handovers.
AWS Step Functions may be excessive for a small synchronous operation that needs no durable state or recovery logic. A direct service call, an event route or an in-process workflow can be simpler when there are few steps and limited operational complexity.
Ask the specialist to walk through a real state-machine design, including failure paths, duplicate events, sensitive data and deployment changes. A capable AWS Step Functions professional can show clear definitions, least-privilege IAM, useful alerts, tests and a practical incident runbook.
Freelancers working with AWS Step Functions should understand Amazon States Language, service integrations and the operational difference between Standard and Express Workflows. They should also account for execution limits, state data, retries, observability and how workflow changes affect running executions.
The average hourly rate of freelancers in Berlin, Germany who have used AWS Step Functions in their recent projects is 84 €, which corresponds to a daily rate of about 671 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used AWS Step Functions in their recent projects, 100% hold at least a Bachelor's degree, 50% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Berlin, Germany who have used AWS Step Functions in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Berlin, Germany who have used AWS Step Functions in their recent projects are German (100%), English (100%), and Hindi (13%).
The most common industries among freelancers in Berlin, Germany who have used AWS Step Functions in their recent projects are Information Technology (88%), Automotive (50%), and Retail (50%).
The most common business areas among freelancers in Berlin, Germany who have used AWS Step Functions in their recent projects are Information Technology (100%), Quality Assurance (88%), and Business Intelligence (75%).
Main locations of FRATCH Experts, who have recently used AWS Step Functions
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.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 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
