Spring Data Expert in Munich
in minutes with vetted, available professionals and the power of AIHire experts who design reliable data access layers, tune Spring Data JPA repositories and connect Spring applications to SQL or NoSQL systems. Get precise matches with vetted, available freelancers who can work remotely or alongside your Munich team.
Meet FRATCH Experts in Munich, who have recently used Spring Data
Fred H.
Last position:
Software Architect and Developer at Personal project
Recurring problem in my own AI-assisted projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but remain difficult to follow 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 form a consistently linked knowledge graph, traceable from requirement to architecture decision – queryable by both people and AI agents. Technically based on RDF/OWL and a custom MCP server.
Result: Working MCP daemon, Docker image published automatically to GHCR, nine hexagonal modules, eleven ADRs (including an Open-Core licensing model). Requirements engineering and Ubiquitous Language hexagons are active. Public as a Community Edition under Apache-2.0 since 07/2026 (github.com/kogn-io/arknet), together with the Claude Code plugin and 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, Interface Development, Software Architecture, Continuous Integration, Knowledge Management
Markus B.
Last position:
Developer / DevOps, Consulting at A state authority in Baden-Württemberg / FSAS Technologies GmbH
Oracle Project Management and Automation
As part of the project, the operation of an Oracle infrastructure was automated by developing and enhancing Puppet modules and migrating existing Bash scripts to a standardized configuration management solution.
The aim of the project was to standardize and automate administrative processes, improve the maintainability of the Oracle platform, and increase the efficiency and traceability of infrastructure management through the use of Infrastructure as Code.
- Analysis and migration of existing Bash shell scripts to a Puppet-based automation solution
- Development and enhancement of Puppet modules for Oracle environments
- Automation of installation, configuration and operational processes using Infrastructure as Code
- Standardization and optimization of configuration management for Oracle systems
- Creation and maintenance of technical and operational documentation
- Planning, coordination and professional support during project implementation
- Operation and automation of Oracle systems on x86-based Linux server platforms
- Testing and quality assurance of the developed Puppet modules
- Analysis and resolution of technical errors as part of the automation and migration
- Close coordination with specialist departments and infrastructure teams to implement automation requirements
Technologies used: Puppet 7/8, SLES 15, Oracle 19, Oracle Grid, SVN, Windows Server, Confluence, VS Code, Ruby, Bash, Pass
Christian T.
Last position:
HDI DevOps & Fullstack Engineer at HDI
- Spring Boot Software Engineer
- DevOps Engineer (Kubernetes, Azure DevOps)
Toolstack: Java, Spring Boot, Kubernetes, Helm, GitOps, ArgoCD, Docker, Azure DevOps, CI/CD Pipelines, Git, Postgres
Enis S.
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.
Maryam N.
Last position:
Senior FullStack Developer at Camunda
- Role: Senior FullStack Developer
- Technologies: Google Cloud, Azure, Microsoft Entra, Keycloak, Spring Boot, Java
- Active in production
Dmitry V.
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
Benedikt B.
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 B.
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.
Janusz M.
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 S.
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
25 years
Position duration
2.3 years
Positions per freelancer
16
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
86%
Doctorate
29%
Certifications per freelancer
1
Most common languages
German, English, Bulgarian
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Spring Data 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 (100%)
- Banking and Finance (90%)
- Automotive (50%)
- Government and Administration (50%)
- Telecommunication (50%)
- Manufacturing (40%)
- Media and Entertainment (40%)
- Retail (40%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data access with Spring
Spring Data is a family of Spring projects that simplifies access to persistence technologies. It provides consistent repository patterns, query methods and mapping support while leaving database-specific behavior to the appropriate module. Companies use it to keep business logic focused and data access easier to test and maintain.
Modules and databases
Spring Data JPA is common in enterprise applications that use relational databases through Jakarta Persistence. Other modules support MongoDB, Redis, Elasticsearch, Neo4j and additional data stores. Strong specialists know where the common repository abstraction helps and where a database-specific query or API is the better choice.
What specialists deliver
- Repository interfaces with derived queries, projections and custom implementations
- Entity mappings, relationships, transactions and auditing for Spring Data JPA
- MongoDB documents, aggregation pipelines and indexing strategies
- Pagination, sorting, caching and reliable data access APIs
- Tests for repositories, persistence behavior and database integrations
Skills around the stack
Effective Spring Data work usually includes Spring Boot, Spring Framework, Java, Kotlin and Jakarta Persistence. Specialists also work with SQL, schema design, migrations, Docker, messaging and observability tools. They understand transaction boundaries, connection pools, lazy loading, query plans and the operational behavior of the chosen database.
When companies engage experts
Freelance expertise helps when a team is replacing hand-written data access, moving from a monolith to services or adding a new persistence technology. It is also useful when slow queries, inconsistent transactions or fragile mappings are affecting production. Munich companies can combine remote delivery with on-site workshops when domain knowledge and close collaboration matter.
Signs of strong expertise
A capable professional starts with access patterns and domain rules rather than adding repositories everywhere. They can explain generated queries, inspect the SQL or database operations produced, and choose between abstraction and direct control. Quality shows in focused tests, clear migration plans, measured performance improvements and documentation that helps the team operate the system after handover.
Frequently asked questions
Everything clients usually want to know about Spring Data, in one place.
Spring Data is used to create a consistent data access layer for Spring applications. It supports repository-based access to relational and NoSQL databases, including query methods, pagination, sorting, auditing and database-specific operations.
Spring Data JPA offers repository abstractions, entity mapping and object-oriented query options, which can reduce repetitive persistence code. Direct JDBC gives finer control over SQL and result handling, so the right choice depends on query complexity, performance needs and the team's comfort with persistence behavior.
A strong Spring Data specialist usually knows Java or Kotlin, Spring Boot, Jakarta Persistence, SQL and database design. Useful adjacent skills include Flyway or Liquibase migrations, Docker, testing with Testcontainers, transaction analysis and application monitoring.
A Spring Data professional should have handled the kind of persistence model, database and access patterns used in the project. Ask for examples involving transaction boundaries, migrations, production query behavior and tests, rather than relying only on repository syntax familiarity.
Spring Data work is often suitable for remote collaboration because code, database schemas and tests can be reviewed asynchronously. On-site sessions in Munich can still help with domain discovery, legacy database analysis and workshops involving several stakeholders.
Spring Data includes modules for systems such as MongoDB, Redis, Elasticsearch and Neo4j. Each module keeps familiar repository concepts while exposing capabilities specific to its database, so specialists must understand document models, indexing and query semantics rather than treating every store like SQL.
Spring Data may be a poor fit when an application needs highly specialized SQL, extremely tight control over generated statements or database features not represented well by its abstraction. A skilled professional can combine Spring Data with custom queries or lower-level access instead of forcing every operation through a repository.
Review whether Spring Data code makes transactions explicit, maps the domain carefully and produces predictable database operations. Look for focused integration tests, sensible indexing, controlled fetch behavior, clear migrations and evidence that performance was checked against realistic access patterns.
The average hourly rate of freelancers in Munich, Germany who have used Spring Data in their recent projects is 96 €, which corresponds to a daily rate of about 764 € 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, 86% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Munich, Germany who have used Spring Data in their recent projects have 25 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Munich, Germany who have used Spring Data in their recent projects are German (100%), English (90%), and Bulgarian (10%).
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 (90%), and Automotive (50%).
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 (60%).
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
