Amazon DynamoDB Experts in Berlin
in minutes from over 15,000 CVs with the power of AI.Hire experts who design DynamoDB data models, tune read and write patterns, and build resilient AWS-backed applications. They handle partition keys, indexes, streams, and event-driven integrations, with fast, precise matching to vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Amazon DynamoDB
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
Oleg Abrazhaev
Last position:
Staff Software Engineer at Kpler Germany GmbH
- Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
- Collaborating with other teams to integrate more domains
Tech stack:
- Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
- BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
- Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD
Maciej Rosiek
Last position:
Full Stack Developer (Freelancer) at Runbuggy
- Led development of RunBot AI assistant autonomously using LLM-powered workflow automation (React, TypeScript, Java, MongoDB, NATS)
- Architected TMS platform providing unified transportation management and real-time logistics visibility with AI processing pipelines
- Designed event-driven microservices architecture supporting marketplace
- Drove architectural decisions and technical leadership across full-stack platform development
Viktor Shcherban
Last position:
AI Engineer (Freelance) at Empion
Enterprise AI content categorization and AI-powered web research.
- Built multi-LLM evaluation framework with annotated data
- Iterated LLM error rates based on annotated datasets
- Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React
Marina Kornilova
Last position:
Independent Software Developer at LILARAUM
- Independently designed, developed, published, and maintained mobile games for iOS and Android.
- Implemented application architecture, gameplay systems, UI, monetization, analytics, and platform integrations.
- Managed the complete release lifecycle, including testing, store publication, production monitoring, and iterative improvements based on analytics.
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
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
Jorge Pérez Suárez
Last position:
Software Engineer – AWS and Kubernetes Specialist at Citti
- Creation, maintenance and hardening of Kubernetes clusters employing Ansible and ArgoCD
- Keywords: Ansible, AWX, Kubernetes, NetApp, Prometheus, CI/CD ArgoCD, SSO, Fluent-bit, HAProxy, Calico, Keycloak, oauth2-proxy, SealedSecrets, kubeseal, Aqua kube-bench, CIS-Benchmarks, Aqua Trivy operator
Utku Erol
Last position:
AI Strategy Consultant at Freelance
- Developed YourBestChance.io, an AI-powered career resilience platform that leverages advanced machine learning to provide personalized guidance and resources for users.
- Architected and implemented a Retrieval-Augmented Generation (RAG) system supporting three languages, utilizing GPT-based large language models (including OpenAI and Grok variants) integrated with specialized vector databases for efficient semantic search and similarity matching.
- Built an interactive AI chatbot powered by generative AI and RAG pipelines to deliver real-time, context-aware responses and enhance user engagement.
- Optimized data pipelines and AI infrastructure for scalability, ensuring robust performance under increasing loads and reducing latency by 50%.
- Developed comprehensive AI strategies using ML and Gen AI to create customized growth plans; analyzed company data to identify strengths, weaknesses, risks, and opportunities for AI integration.
- Defined ethical frameworks for AI deployment, assessed workforce and leadership upskilling needs, and built phased action plans (short-, mid-, and long-term) with targeted AI integration recommendations.
Volker Krause
Last position:
Head of Engineering at Infoniqa
- Led engineering execution: roadmap planning, capacity alignment, risk management, dependencies, and delivery tracking.
- Consolidated multiple payroll product lines into a unified SaaS platform on Dynamics 365 Business Central, enabling scalable post-merger operations and reducing operational complexity across the portfolio.
- Restructured engineering and product teams in a remote-first setting across Germany, Austria and Poland, consisting of five cross-functional units: compliance/enabling, platform, DevOps and two stream-aligned teams with total FTE depending on phase of reorganisation.
- Rebuilt the mid-level leadership layer and mentored engineering leaders, establishing a leadership pipeline and strengthening architectural decision-making across teams for scalable growth, delivery ownership and predictability.
- Designed platform foundations and system boundaries using Team Topologies aligned structures, enabling scalable ownership, clear interfaces and parallel development across distributed teams.
- Spearheaded AI transformation by implementing AI-assisted SDLC practices using SpecKit and GitHub Actions for automated, executable specifications, while delivering agentic product capabilities by securely exposing platform data and services to AI agents and copilots via RAG-based retrieval pipelines and MCP-style extensions.
- Drove modularisation of tightly coupled legacy logic into independently deployable services, improving maintainability, testability and architectural clarity while preserving continuity through targeted, low-risk extraction rather than full rewrites.
- Established observability, CI/CD and DevOps governance as platform capabilities, increasing automated compliance gates from 25% to 75% and improving deployment cadence by 40% across 15+ product versions.
- Improved operational resilience using DORA-aligned practices (lead time ↓50%, SaaS MTTR ↓85%), strengthening reliability and reducing support overhead.
- Coordinated engineering recovery for the German payroll platform during a company-wide P0 ransomware incident; restored platform continuity within 72h, validated data integrity, and rolled out hardened runbooks and automated recovery playbooks.
- Responsible for budget compliance and cost oversight in Engineering, with limited P&L responsibility and participating in the annual COGS/OPEX/CAPEX planning cycle.
Meisam Ghafarlangroudi
Last position:
Senior AI Engineer / Data Scientist at Geeks Ltd (WordUp)
Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.
- Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
- Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
- Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
- Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
- Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.
Khaled Mohamed
Last position:
Senior/Staff Backend Engineer at Heycar (Mobility Trader GmbH)
- Heycar is a leading automotive platform redefining the used car market through intelligent data pipelines, multi-tenant services, and dealer-focused tooling. I led initiatives across backend architecture, data ingestion, and identity management to enhance scalability, reliability, and developer productivity.
- Ingestion Platform: Designed a unified ingestion platform with YAML-based configuration, enabling new dealer data pipelines to be onboarded without code changes, cutting setup time from ~2 months to 2-3 days and improving scalability by 40%.
- Keycloak Leadership: Acted as the company's Keycloak expert, scaling it for multi-tenant identity management and extending functionality with custom plugins and delegated admin APIs.
- Back-Office Tooling: Developed a back-office application integrated with Salesforce, enabling dealers to manage inventory, convert leads, and handle support requests in real time.
- Multi-Tenant Migration: Collaborated across backend teams to migrate Heycar's core services into a unified multi-tenant cluster, ensuring high availability.
- Frontend CI Optimization: Optimized monorepo delivery by implementing CircleCI dynamic config with NX, deploying only affected UI projects and drastically cutting build times.
- Observability & Mentorship: Enhanced monitoring and release reliability while mentoring backend engineers and improving code review standards.
Shyam Sundar Rampalli
Last position:
GenAI Engineer at Freelance
- Built a hybrid semantic and keyword search and LLM-based requirement extraction from conversational queries, boosting search accuracy by 85%, cutting zero-result searches by 70%, and reducing search time by 60%.
- Deployed a production-ready API with monitoring dashboards over 100K+ products, keeping response times under 2s and reducing customer search-to-purchase time by 40%.
- Technologies: Python, BGE-M3, Qwen2.5, FastAPI, Qdrant, Meilisearch, Docker, Prometheus, vLLM.
Christian Richter
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Luca Demmel
Last position:
CTO & Co-Founder at Lucent Data GmbH
- Built a warehouse native customer data platform from scratch
- Successfully onboarded the first customer
- Built the first engineering team of 3 engineers
- Raised 1M€ in venture capital
Discover over 15,000 top freelancers
Statistics of experts using Amazon DynamoDB
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
2.3 years (Germany: 2.1 years)
Positions per freelancer
9 (Germany: 11)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Retail, Automotive
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
95% (Germany: 91%)
Master's degree or higher
55% (Germany: 53%)
Doctorate
5% (Germany: 9%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, Russian
Speak two or more languages
100%
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 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 Amazon DynamoDB
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
What DynamoDB is
Amazon DynamoDB is AWS's managed NoSQL database for applications that need predictable latency and elastic scaling. It stores key-value and document data, which makes it a fit for user profiles, product catalogs, session stores, event logs, and other workloads where simple access patterns matter.
Where it fits
- Serverless back ends on AWS
- High-traffic APIs and mobile apps
- Event-driven systems with streams
- Single-table designs for fast reads
DynamoDB is often chosen when teams want to avoid operating a database cluster and need a service that can grow with demand.
Core ecosystem
Strong professionals working with DynamoDB usually also know AWS Lambda, API Gateway, IAM, CloudWatch, and DynamoDB Streams. They understand partitioning, secondary indexes, backup and restore, and how to connect the database to analytics, queues, and search services without breaking access patterns.
When companies bring in specialists
Companies usually look for freelance expertise when a DynamoDB table grows messy, slow, or expensive to query. They also bring in specialists for new product launches, migrations from relational systems, schema redesign, and production incidents that need careful diagnosis.
What good work looks like
Good DynamoDB work starts with access patterns, not with tables. A strong specialist designs keys, chooses indexes sparingly, and avoids hot partitions, oversized items, and unnecessary scans.
Berlin collaboration
In Berlin, DynamoDB work often supports SaaS products, fintech tools, logistics services, and marketplace back ends. Teams may need on-site workshops for data modeling, while implementation and review are often handled remotely with clear documentation and English as the working language.
Frequently asked questions
Quick answers to the questions that come up most around Amazon DynamoDB.
Amazon DynamoDB is used for applications that need fast and predictable access to structured data without managing database servers. It fits user sessions, shopping carts, event tracking, feature flags, and other workloads where key-based reads and writes are the main pattern.
DynamoDB is not a replacement for every relational database. It works better when access patterns are known in advance and scale, availability, and low-latency reads matter more than joins and ad hoc queries. If your data model depends on complex reporting or many cross-table relationships, a relational system may be a better fit.
Amazon DynamoDB is fully managed on AWS and tightly integrated with AWS services, which reduces operational work. Compared with MongoDB, it is more opinionated around key-based access; compared with Cassandra, it removes much of the cluster management but still requires careful data modeling. The right choice depends on how your application reads and writes data.
A strong DynamoDB specialist should understand data modeling, partition keys, sort keys, GSIs, LSIs, Streams, and IAM. AWS experience matters too, especially with Lambda, CloudWatch, and infrastructure as code, because DynamoDB is usually part of a wider system.
Most teams bring in Amazon DynamoDB expertise when the schema is still flexible or when a live system starts to show bottlenecks. That is the right time to fix access patterns, check partition design, and avoid costly rework later. Early input is often more valuable than late cleanup.
Yes, DynamoDB work is often well suited to remote collaboration because data modeling, review, and troubleshooting can be done through diagrams, logs, and sample requests. In Berlin, some teams prefer an on-site workshop at the start, then continue remotely for implementation and follow-up.
If Amazon DynamoDB queries rely on scans, latency is uneven, or a few keys receive too much traffic, the design likely needs review. Other warning signs are growing item sizes, too many indexes, or application logic that works around the table instead of using it cleanly.
Look for clear reasoning about access patterns, partition keys, and index usage in the first conversation. A good DynamoDB professional explains trade-offs, asks about workloads and failure modes, and can show how they reduced complexity rather than adding more moving parts.
The average hourly rate of freelancers in Berlin, Germany who have used Amazon DynamoDB in their recent projects is 94 €, which corresponds to a daily rate of about 751 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Amazon DynamoDB in their recent projects, 95% hold at least a Bachelor's degree, 55% hold at least a Master's degree, and 5% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Amazon DynamoDB in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Berlin, Germany who have used Amazon DynamoDB in their recent projects are German (95%), English (95%), and Russian (14%).
The most common industries among freelancers in Berlin, Germany who have used Amazon DynamoDB in their recent projects are Information Technology (95%), Retail (57%), and Automotive (48%).
The most common business areas among freelancers in Berlin, Germany who have used Amazon DynamoDB in their recent projects are Information Technology (100%), Product Development (90%), and Project Management (62%).
Main locations of FRATCH Experts, who have recently used Amazon DynamoDB
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.
Hamburg 
Munich 
Cologne 
Frankfurt 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
