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AWS Step Functions Experts in Berlin

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Hire experts who design state machines, connect Lambda, ECS and SNS steps, and build reliable serverless workflows for approvals, retries, and orchestration. FRATCH matches you fast and precisely with vetted, available freelancers.

Meet FRATCH Experts in Berlin, who have recently used AWS Step Functions

Verified expert

Deepak Mishra

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Lead ML Platform Engineer

Berlin
Deepak Mishra

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
Verified expert

Lasya Marella

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Data Engineer

Berlin
Lasya Marella

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.
Verified expert

Jan Krol

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Data Expert

Berlin
Jan Krol

Last position:

Data Expert at Manufacturing

Verified expert

Qaiser Abbasi

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Freelance Lead DevOps Engineer

Berlin
Qaiser Abbasi

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

Verified expert

Vili Dhamo

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Senior Data Engineer, Data Architect, Software Engineer

Neuenhagen
Vili Dhamo

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
Verified expert

Amit Ghodke

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Authorized Officer - Software Engineer

Berlin
Amit Ghodke

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.

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 30 Aug 2026.

Daily rate distribution

0 1 2 3 4
<€320 €320-​480 €480-​640 €640-​800 €800-​960 €960+

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.

800
600
400
200
Rate comparison chart
Daily rate avg. 670 €

The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.

800
600
400
200
Rate comparison chart
Median rate 700 €

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

Workflow orchestration

AWS Step Functions is used to coordinate serverless and container-based processes across multiple AWS services. It fits order flows, data pipelines, approval chains, batch jobs, and event-driven backends that need clear control over each step.

Typical delivery

  • State machines for branching, retries, and error handling
  • Orchestration for Lambda, ECS, SNS, SQS, DynamoDB, and API calls
  • Express and Standard workflows for different runtime needs
  • Step-by-step service integration without custom glue code

Ecosystem fit

Strong specialists work with JSON or Amazon States Language, CloudWatch logs, IAM permissions, and event sources from EventBridge or API Gateway. They also know where Step Functions should stop and where a simpler Lambda chain, queue, or workflow engine is a better fit.

When to bring help

Companies usually bring in freelance expertise when a workflow fails under edge cases, retry logic becomes hard to maintain, or a new service must join an existing orchestration. In Berlin, this often comes up in product teams, logistics, fintech, and platform work that mixes cloud services with local delivery teams.

What strong specialists do

  • Model clean state transitions and failure paths
  • Keep workflows readable, testable, and observable
  • Tune for cost, latency, and maintainability
  • Align IAM, logging, and deployment with the wider AWS setup

Signals of quality

A strong AWS Step Functions specialist explains trade-offs clearly and spots when the workflow design is too complex. They write for long-term maintenance, not just a passing demo, and they can work with remote teams or join Berlin-based workshops when deeper system alignment is needed.

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Frequently asked questions

Key details about AWS Step Functions, drawn from the questions we get asked most.

AWS Step Functions is used to orchestrate multi-step workflows across AWS services. Teams use it for order processing, data processing, scheduled jobs, approvals, and event-driven automation where retries and branching matter. It is a good fit when a process needs clear state and visible execution flow.

A AWS Step Functions workflow makes the process explicit, while a Lambda chain often hides the control logic in code. Step Functions is usually easier to read when there are retries, fallback paths, pauses, or parallel branches. If the flow is simple and short-lived, a direct Lambda call can still be enough.

A strong Step Functions specialist usually knows Lambda, IAM, CloudWatch, EventBridge, SQS, SNS, and ECS. They should also understand JSON-based workflow design, error handling, and how to expose good logs for support teams. Infrastructure as code is often part of the work as well.

AWS Step Functions work can be small or very complex, but the quality bar rises fast once workflows span many services. For a simple approval or routing flow, a focused specialist may be enough. For mission-critical orchestration, you want someone who has handled failures, observability, and deployment patterns before.

Bring in AWS Step Functions expertise when a workflow becomes hard to debug, the current state machine is messy, or the team needs to add new branches without breaking existing behavior. Freelancers are also useful when you need a quick design review before launch. This is common in Berlin teams that need short, targeted support instead of a long hiring cycle.

AWS Step Functions can be designed and reviewed well in remote collaboration because the work is mostly about architecture, diagrams, and code reviews. On-site time helps when many teams are involved or when the workflow touches sensitive business operations. In Berlin, a hybrid setup often works well for workshops and handover sessions.

Look for a Step Functions expert who can explain the state machine in plain language, not just show a working demo. Good signs are clean error paths, readable state names, sensible retries, and logging that helps operations teams. They should also know when Step Functions is the right tool and when a simpler design is better.

The most common comparison is with custom orchestration in code, usually around Lambda or a service workflow layer. Some teams also compare AWS Step Functions with workflow tools, message-driven designs, or other cloud orchestration services. The right choice depends on how much control, visibility, and AWS integration the project needs.

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 670 € 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

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