
Amazon Kinesis Experts in Berlin
, matched in minutes from over 15,000 CVsHire experts who design streaming architectures, connect Kinesis Data Streams with AWS analytics services, and deliver event-driven data pipelines for applications and operations. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Amazon Kinesis
Alexander Z.
Last position:
Senior Data Solutions Engineer at VMware Inc.
- Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
- Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
- Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
- Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Santhosh K.
Last position:
Freelance Software Engineer at Zalando SE
- Drive migration of enterprise authorization platform from Styra DAS to open-source OPA via Skipper (Zalando's Golang-based ingress proxy) integration
- Optimise k8s resources and integrate native Prometheus metrics with OPA
- Migrate from internal monitoring solution to Prometheus CRs + Dash0
Tech Stack: Java/Kotlin, Golang, Python, Spring Boot, AWS, Kubernetes, Docker, OpenTofu, Prometheus, Grafana
Jan K.
Last position:
Data Expert at Manufacturing
Meisam G.
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 M.
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.
Christian R.
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Ilya I.
Last position:
Data/Platform/Software Engineer/SRE at IT Consulting
- Designed a platform based on IoT, Azure, Kubernetes, and Postgres for an existing application
- Migrated from "click-ops" and UI-defined CI/CD pipelines to infrastructure-as-code with Terraform, enabling complete redeployment of multiple environments
- Technologies: Terraform, OpenTofu, Azure, Azure DevOps, Kafka, IoT, Kubernetes, Grafana, Prometheus, GitOps, relational databases
Henning P.
Last position:
Backend Systems Development at Deutsche Bank/DWS/Morgenfund
- Developed backend systems for the DWS Investment App, a white-label investment solution (robo-advisor), and an investment solution for institutional clients.
- Java 8-17, Kotlin, Spring Boot, Spring MVC, OpenAPI 3.0, JPA, JDBI, Oracle, Hazelcast, JXLS, Apache POI, Apache PDFBox, Apache Kafka & Avro, Active MQ, Elasticsearch, React Native, Spock Test, IntelliJ IDEA, Kubernetes, Helm, Microservices/Netflix-Stack, TeamCity, Splunk.
- Kanban team, continuous integration.
- Migrated existing applications from Deutsche Bank's private cloud environment to Azure as part of a carve-out.
Discover over 15,000 top freelancers
Statistics of experts using Amazon Kinesis
Aggregated from the professional profiles of matched freelancers.
Experience
18 years (Germany: 16 years)

Position duration
2.8 years (Germany: 2.3 years)

Positions per freelancer
9 (Germany: 10)

Top business areas
Information Technology, Product Development, Business Intelligence

Top industries
Information Technology, Retail, Automotive
Bachelor's degree or higher
86% (Germany: 93%)
Master's degree or higher
29% (Germany: 56%)

Certifications per freelancer
1 (Germany: 5)

Most common languages
German, English, Arabic

Speak two or more languages
100% (Germany: 97%)
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 Amazon Kinesis
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.
Amazon Kinesis 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%)
- Retail (50%)
- Automotive (38%)
- Banking and Finance (38%)
- Professional Services (38%)
- Telecommunication (38%)
- Transportation (25%)
- Media and Entertainment (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Real-time streaming
Amazon Kinesis is an AWS service family for collecting, processing and analyzing continuously generated data. Companies use Kinesis Data Streams for application events, telemetry, clickstreams and operational records that must be handled as they arrive. Kinesis Data Firehose delivers streaming data to destinations such as Amazon S3, Redshift and OpenSearch Service.
Core components
Kinesis Data Streams provides durable shards, partition keys and ordered records within a stream. Kinesis Data Firehose manages buffering and delivery, while Kinesis Data Analytics supports SQL-based analysis of streaming data. Strong specialists also work with enhanced fan-out, retention settings, resharding and stream monitoring.
Typical projects
- Route application and IoT events into analytics or storage systems
- Build real-time dashboards, alerts and operational monitoring
- Feed data lakes and warehouses through managed delivery streams
- Process clickstreams, logs and transactions with event-driven services
- Integrate Kinesis with Lambda, Glue, EMR and Amazon OpenSearch Service
When expertise matters
Freelance expertise helps when a team is moving from batch jobs to streaming, replacing fragile ingestion scripts or designing a new AWS data platform. It is also valuable when throughput, ordering, replay, schema changes or delivery failures create production risks. In Berlin, remote collaboration is common, while some data-sensitive projects still benefit from on-site workshops.
Skills around Kinesis
A capable professional combines Kinesis knowledge with AWS Identity and Access Management, CloudFormation or Terraform, Lambda and CloudWatch. They understand partition-key design, back-pressure, consumer coordination, idempotency and failure recovery. Experience with Python, Java or Node.js, plus Kafka or Spark, helps when systems must connect across cloud and data ecosystems.
Choosing a specialist
Look for clear explanations of stream topology, retention, scaling and cost controls rather than familiarity with service names alone. Ask for examples of monitoring lag, handling duplicate records and recovering from downstream outages. The strongest professionals define measurable delivery behavior, secure every integration and leave behind tested infrastructure, runbooks and useful dashboards.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Amazon Kinesis.
Amazon Kinesis is used to ingest and process data that arrives continuously, including events, logs, telemetry, transactions and clickstreams. Companies use it to power real-time dashboards, alerts, analytics pipelines and data-lake delivery.
Amazon Kinesis is a managed AWS service with integrated connections to Lambda, Firehose, S3, Redshift and other AWS tools. Apache Kafka can offer broader ecosystem flexibility and portability, but it usually requires more decisions around operating, scaling and securing the platform.
A strong Amazon Kinesis specialist usually understands IAM, Lambda, CloudWatch, S3, Glue and infrastructure as code with Terraform or CloudFormation. Python, Java or Node.js experience is useful, as are skills with Kafka, Spark, schema management and data-quality controls.
The right level depends on the workload, reliability requirements and existing AWS setup, not on a fixed number of years. For a production stream, seek someone who can explain partition-key choices, consumer behavior, replay, monitoring, security and failure recovery using relevant project examples.
Amazon Kinesis work is often suitable for remote collaboration because architecture, infrastructure and code can be reviewed asynchronously. Berlin-based teams may still prefer occasional on-site sessions for data-governance workshops, incident planning or coordination with German-speaking stakeholders.
A Kinesis engagement should produce a documented stream design, provisioned infrastructure, secure access policies and tested producers and consumers. It should also include monitoring for iterator age and delivery failures, operational runbooks, recovery procedures and clear handover documentation.
Ask the Amazon Kinesis professional to trace one record from ingestion through processing and final delivery. Their answer should cover ordering limits, duplicates, retries, back-pressure, partition hot spots, observability and what happens when a downstream service is unavailable.
Within Amazon Kinesis, Firehose is often the simpler choice when data mainly needs managed delivery to destinations such as S3, Redshift or OpenSearch Service. Data Streams is better when applications need custom consumers, fine-grained processing, replay control or more direct handling of records.
The average hourly rate of freelancers in Berlin, Germany who have used Amazon Kinesis in their recent projects is 93 €, which corresponds to a daily rate of about 746 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Amazon Kinesis in their recent projects, 86% hold at least a Bachelor's degree and 29% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Amazon Kinesis in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Berlin, Germany who have used Amazon Kinesis in their recent projects are German (100%), English (100%), and Arabic (13%).
The most common industries among freelancers in Berlin, Germany who have used Amazon Kinesis in their recent projects are Information Technology (88%), Retail (50%), and Automotive (38%).
The most common business areas among freelancers in Berlin, Germany who have used Amazon Kinesis in their recent projects are Information Technology (100%), Product Development (75%), and Business Intelligence (63%).
Main locations of FRATCH Experts, who have recently used Amazon Kinesis
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.
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