AWS Step Functions Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used AWS Step Functions
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.
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
Jan K.
Last position:
Data Expert at Manufacturing
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
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, 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
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.
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 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin 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 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 19 Sep 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
What It Does
AWS Step Functions is a managed workflow orchestration service for coordinating applications and AWS services. Teams define state machines that control tasks, choices, parallel branches, retries and error handling without managing orchestration servers. It supports both Standard and Express workflows for different execution patterns.
Typical Workflows
Step Functions is used to make complex application processes visible, controlled and repeatable.
- Coordinate Lambda functions and container tasks
- Manage approval, payment and fulfilment flows
- Run data and machine learning pipelines
- Orchestrate microservices and event-driven processes
- Handle retries, timeouts and human interaction
Ecosystem Skills
Strong specialists work with Amazon States Language, JSON-based state machine definitions and the AWS SDK integrations. They commonly connect Step Functions with Lambda, Amazon EventBridge, Amazon SQS, Amazon SNS, Amazon ECS, Amazon DynamoDB and AWS Batch. Infrastructure as code through AWS CloudFormation, AWS CDK or Terraform helps teams version and deploy workflows safely.
When to Bring Expertise
Companies often need freelance support when a workflow spans several services, has difficult failure paths or is replacing custom orchestration code. Specialists can map business states, choose Standard or Express workflows, set execution boundaries and improve observability. In Berlin, remote collaboration is common, while on-site workshops may help align product, operations and cloud teams.
- Replace brittle scripts or chained functions
- Separate workflow logic from application code
- Investigate failed and duplicated executions
- Prepare a secure production rollout
Quality Signals
A capable AWS Step Functions professional models success and failure paths before writing definitions. They use least-privilege IAM policies, idempotent tasks, meaningful timeouts and deliberate retry strategies. They also understand service quotas, payload limits, logging, tracing and the operational cost of long-running executions.
Project Outcomes
Good implementations leave behind readable state machines, tested integrations and clear ownership of each task. They include alarms, dashboards and runbooks for common failures rather than relying on manual investigation. The result is a workflow that can evolve as services, business rules and compliance needs change, with documentation that lets the internal team operate it confidently.
Frequently asked questions
Key details about AWS Step Functions, drawn from the questions we get asked most.
AWS Step Functions coordinates multiple application steps and AWS services in a visual, state-based workflow. Companies use it for order processing, data pipelines, approval flows, microservice orchestration and other processes that need retries, branching or controlled failure handling.
AWS Step Functions manages ordered, stateful processes with explicit transitions, execution history and error handling. Amazon EventBridge is better suited to routing events between systems, while tools such as Apache Airflow often focus on scheduled data workflows and may require more operational ownership.
A strong AWS Step Functions specialist usually understands Lambda, IAM, EventBridge, SQS, SNS, ECS and DynamoDB. Experience with Amazon States Language, AWS CDK, CloudFormation or Terraform, plus logging and distributed tracing, is valuable for production work.
The right AWS Step Functions experience depends on workflow risk and integration depth, not on a fixed duration. A straightforward Lambda flow may need focused implementation support, while regulated or long-running processes call for a specialist who has handled idempotency, recovery, access control and operational handover.
AWS Step Functions work is well suited to remote collaboration because definitions, infrastructure and reviews are managed in code. Teams in Berlin can work effectively with remote specialists when ownership, documentation, meeting language and access to AWS environments are agreed at the start; on-site workshops can support complex process mapping.
The choice between Standard and Express in AWS Step Functions depends on execution duration, volume, visibility and delivery requirements. Standard workflows suit durable, auditable processes with detailed execution history, while Express workflows fit high-throughput processing where shorter-lived execution patterns and different monitoring needs are acceptable.
Review whether AWS Step Functions definitions make business states and failure paths easy to understand. Quality work includes least-privilege IAM, idempotent tasks, useful timeouts, tested retries, clear alarms, traceable logs and documentation for operating and recovering workflows.
Before starting AWS Step Functions work, clarify the source events, task ownership, payloads, retry policy, execution duration and recovery expectations. The specialist should also confirm deployment tooling, AWS account boundaries, observability standards and whether the team needs coaching alongside delivery.
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 669 € 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
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Philipp Thomaschewski
FRATCH CEO
