Spring Data Experts in Munich
in minutes from 15,000 CVs with the power of AIHire experts who know Spring Data repositories, Spring Data JPA, and data access for MongoDB, Redis, and SQL-backed services. Get support for clean persistence layers, query tuning, and Spring Boot integration, matched fast and precisely with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Spring Data
Fred Hauschel
Last position:
Software Architect and Developer at Personal project
A recurring problem in my own AI-supported projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but they remain hard to trace and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions as a continuously linked knowledge graph, traceable from the requirement to the architecture decision – queryable for both people and AI agents alike. Technically based on RDF/OWL and its own MCP server.
Result: MCP daemon running, Docker image automatically published on GHCR, nine hexagonal modules, eleven ADRs (including an open-core licensing model). Requirements engineering and ubiquitous language hexagon active. Publicly available since 07/2026 as a Community Edition under Apache-2.0 (github.com/kogn-io/arknet), together with the Claude Code plugin and the GHCR image; open-core model.
Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ
Enis Spahi
Last position:
Software Developer at 50Hertz Transmission GmbH
- Participated in the gradual modernization of components into cloud-native 12-factor applications.
- Worked closely with the business operations team to eliminate manual processes and resolve several performance bottlenecks.
- Designed and implemented a CI/CD pipeline to increase developer productivity, enforce quality and security checks, and automate product delivery.
- Migrated several components into the OpenShift Kubernetes cluster.
- Built a monitoring stack from scratch with Prometheus and Grafana to monitor services running in OpenShift.
- Developed dashboards in both Grafana and Splunk for operational transparency.
- Implemented an OIDC/OAuth2-based single sign-on (SSO) solution with Keycloak to secure multiple applications.
- Technologies: Java, Spring, Quarkus, Kafka, MySQL, Cassandra, Redis, Spring Data, Hibernate, Docker, Kubernetes, OpenShift, Keycloak, OIDC, OAuth2, Helm, Prometheus, Grafana, Splunk, Spark.
Dmitry Varlamov
Last position:
Fullstack Software Developer at Mercedes-Benz Tech Innovation
- Backend development of the cloud-based microservice
- Frontend development of the microfrontend
- Migration of the cloud backend environment
- Rollout of the distributed high-availability Privacy Data Service
- Technologies: Java, Kotlin, Spring Boot, REST Services, Kubernetes, Microsoft Azure, Cloud Security, OpenAPI, Postman, JavaScript, Vue.js, AngularJS, TypeScript, Micro Frontend, Redis, Kibana, Grafana, CI/CD, GitHub Actions, Jenkins, Helm Charts, Sec-Hub, Black Duck, Docker, Maven, Git, Scrum
Maryam Nemati
Last position:
Senior FullStack Developer at Camunda
- Role: Senior FullStack Developer
- Technologies: Google Cloud, Azure, Microsoft Entra, Keycloak, Spring Boot, Java
- Active in production
Benedikt Buchner
Last position:
Fullstack Developer at Nimevio
- Requirements analysis and planning of the software architecture
- Analysis and design of REST APIs
- Backend development with Java 17, Spring Boot, Spring MVC, and Spring Data
- Frontend development with Angular and TypeScript
- Setting up CI/CD pipelines
- Code review, QA, and testing
- Using MySQL, Docker, the ELK stack, and RabbitMQ
Bela Bocsak
Last position:
Full Stack Lead Developer, Backend Architect at Telefonica (O2)
The software supports the complete planning and approval of antennas for mobile telephony.
The system was implemented using an event-driven microservice architecture for cloud-native deployment with Quarkus on the backend, Kafka for communication, and Angular for the frontend. Services run on Kubernetes in Google Cloud. A special challenge was synchronizing with the legacy system still used by some users.
Christian Trutz
Last position:
JEE Software Engineer, DevOps Engineer at AKDB
- JEE Software Engineer
- DevOps Engineer
- Java, JEE, JBoss/WildFly, Vaadin, CI/CD Pipelines, Jenkins, Maven, Git, Oracle database, MSSQL Server database
Janusz Mazurek
Last position:
IoT Edge Computing / Self-Driving-Cars at Automotive consulting company
- Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
- Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
- Responsible for webinar:
- IoT edge computing: architecture, components, resources, management
- IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
- IoT processes, connectivity, data transfer and deployment, security
- Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
- Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
- Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
- Analysis of large sensor data sets with Apache Spark, Kafka clusters
- Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
- Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
- Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))
Rangel Stefanov
Last position:
Senior Software Consultant at Freelancer
Discover over 15,000 top freelancers
Statistics of experts using Spring Data
Aggregated from the professional profiles of matched freelancers.
Experience
24 years
Position duration
2.7 years
Positions per freelancer
13
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
33%
Certifications per freelancer
1
Most common languages
German, English, Bulgarian
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 Munich 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 Munich using Spring Data
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
Data access made clean Spring Data helps teams build the persistence layer without writing repetitive boilerplate. It gives a common model for repositories, queries, paging, and auditing, while staying close to Spring and Spring Boot. Teams use it for services that need reliable access to relational and NoSQL data.
Common Spring Data modules
- Spring Data JPA for relational databases and ORM-based services
- Spring Data MongoDB for document storage and flexible schemas
- Spring Data Redis for caching, sessions, and fast key-value access
- Repository abstractions, query methods, and custom data access code
- Auditing, projections, paging, and sorting
When specialists help Companies bring in freelance specialists when a codebase grows, queries get hard to maintain, or data access is spread across too many layers. That is common in Munich teams working on enterprise software, mobility, fintech, SaaS, and internal platforms. Strong experts can stabilize legacy Spring Data JPA code, introduce better repository patterns, and reduce fragile database logic.
What strong experts deliver A good Spring Data professional understands entity mapping, transaction boundaries, lazy loading, and how repositories fit into service design. They can work with JPA, Hibernate, MongoDB, and other Spring Data modules without forcing the same pattern everywhere. They also keep an eye on testability, schema changes, and performance under real load.
Skills around it
- Java and Spring Boot service design
- JPA, Hibernate, and SQL database modeling
- MongoDB, Redis, and other Spring Data stores
- Query optimization and transaction handling
- Testing with integration tests and realistic fixtures
How companies use it Spring Data appears in REST APIs, event-driven services, admin backends, and data-heavy business applications. It is a strong fit when teams want consistent access patterns across several storage systems. In Munich, it often shows up in projects that need careful integration with existing Spring stacks and clear collaboration in English or German.
Frequently asked questions
Everything clients usually want to know about Spring Data, in one place.
Spring Data is used to simplify data access in Spring-based applications. It is common in REST APIs, back-office systems, and services that read and write to SQL or NoSQL stores. Teams use it to keep repository code smaller and easier to maintain.
No. Spring Data is the wider family of data-access modules, while Spring Data JPA is the part focused on JPA and relational databases. People often say Spring Data when they really mean Spring Data JPA, so it helps to check which store and abstraction the project needs.
Spring Data adds repository abstractions, query derivation, paging, and a more consistent style across data stores. Plain JPA or JDBC can be better when a team wants full control over SQL or persistence behavior. A strong specialist knows when the abstraction helps and when it gets in the way.
A good Spring Data specialist usually also knows Spring Boot, JPA, Hibernate, SQL modeling, and testing. For non-relational work, MongoDB or Redis experience is often important. Understanding transactions and performance tuning matters just as much as framework syntax.
Spring Data help is useful as soon as repository design, transactions, or query behavior become hard to change safely. You do not need a full rewrite to benefit. Many companies bring in a specialist to fix one service, set patterns, or review a risky migration.
Spring Data work is often done remotely because the main tasks are in code, tests, and design discussions. On-site time can help when the team needs to align on domain rules, schema changes, or a shared persistence strategy. In Munich, many teams mix both depending on the project phase.
Look for clear choices around repository design, transaction handling, test coverage, and performance. A strong Spring Data professional can explain why a query method is enough in one place and why a custom implementation is better in another. Good signs are clean boundaries, stable tests, and code that matches the data model.
Spring Data is often brought in to reduce bulky DAO code, untangle custom query logic, and make old persistence layers easier to extend. It can also help when a system mixes JPA, MongoDB, or Redis and needs one clearer approach. The best results come from careful refactoring, not a rushed rewrite.
The average hourly rate of freelancers in Munich, Germany who have used Spring Data in their recent projects is 98 €, which corresponds to a daily rate of about 785 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Spring Data in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Munich, Germany who have used Spring Data in their recent projects have 24 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers in Munich, Germany who have used Spring Data in their recent projects are German (100%), English (89%), and Bulgarian (11%).
The most common industries among freelancers in Munich, Germany who have used Spring Data in their recent projects are Information Technology (100%), Banking and Finance (89%), and Automotive (44%).
The most common business areas among freelancers in Munich, Germany who have used Spring Data in their recent projects are Information Technology (100%), Product Development (100%), and Project Management (56%).
Main locations of FRATCH Experts, who have recently used Spring Data
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
