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Andriy L., Data Science / Java Software Engineer

Ternopil, Ukraine
Last Updated: 4 Jul 2023
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• 10+ year experience with JAVA and Linux operating systems: Java 11, Spring Boot, Spring Data JPA, MySQL/MongoDB, ElasticSearch, Jenkins CI • Big Data, Data Engineering, NLP, Corpus Linguistics, ML, DL • Design and implement a distributed warehouse system (AWS migration), REST API design and cache implementation (1,000-10,000 requests/sec), Design and implement high-load microservices • Deliver special courses devoted to concurrency and prepared handbook on “Parallel and Distributed Computations” [technologies: (basics of OpenMP, MPI, CUDA, OpenCL), advanced in Java concurrency]; • Scientific projects at university related research increasing effectiveness of crypto-analysis • Upper-Intermediate English • Available Full-time • Ready to start in 2 weeks • No scheduled vacations within next 3months

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Java

Java   15 yr.

Hadoop ecosystem

Hadoop ecosystem

Python

Python   3 yr.

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Maksym, Java (Kotlin) Developer

Ukraine
Last Updated: 4 Jul 2023

- 4+ years of commercial experience with Java and 3+ years of commercial experience with Kotlin - Proficient in Java and Kotlin programming languages, with experience in Spring Boot, Spring Core, Spring Data, Spring web, and Spring Cloud frameworks - Skilled in developing and maintaining APIs for mobile and tablet apps, including Kotlin-based Screwfix app development and Java-based NextGear Capital Mobile Account Portal - Expertise in Gradle, Jenkins, GitLab CI/CD, Docker, Microservice Architecture, Scrum, and various databases and cloud platforms - Demonstrated ability to solve architectural issues and create new features, with experience in both front-end and back-end development - Worked mainly with Spring Framework stack, and different REST APIs for mobile apps and tablets, creating and maintaining them with Java and Kotlin. - Upper-Intermediate English

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Java

Java   4 yr.

Kotlin

Kotlin   3 yr.

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Illya T., Java Software Engineer

Ukraine
Last Updated: 4 Jul 2023

- 3+ years experience in the IT industry as a Java developer - Has skills in developing microservice applications - Has experience in programming Spring Framework, Hibernate - Great knowledge of Java Core - Upper-Intermediate English - Available ASAP

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Java

Java

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Dmytro Sh., Java Software Engineer

Poland
Last Updated: 27 Sep 2023

- 5+ years experience in Java and related technologies. - Good knowledge in programming Spring Framework, Hibernate, and Java Core. - Experience in creating microservices architecture APIs from scratch and deploying them. - Participated in the discussion of API architecture and technology stack. - Proficient in writing integration tests. - Upper-Intermediate English

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Java

Java

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Vladyslav A., Java Software Engineer

Poland
Last Updated: 26 Sep 2023

- 5+ years experience in the IT industry working with Java and related technologies. - Good knowledge in programming Spring Framework, Hibernate, and Java Core. - Has experience with microservices architecture. - In-depth knowledge of database systems, especially PostgreSQL, and related migration tools like Flyway and Liquibase. - Proficient in AWS services such as Elastic Kubernetes Service (EKS), PostgreSQL, DynamoDB, S3, and more. - Proficiency with messaging systems like Kafka and RabbitMQ. - Demonstrated history of working on innovative projects within the green technology domain, including sophisticated statistical analysis for order processing. - Proficient in implementing and maintaining security protocols and access control using Spring Security. - Upper-Intermediate English.

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Java

Java

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Table of Contents

What are top Spring Data instruments and tools?

  • Spring Data JPA: Spring Data JPA is a part of the larger Spring Data family and provides an easy and efficient way to implement JPA-based repositories. It simplifies the development of data access layers by providing automatic repository implementations, query generation, and transaction management. It was first released in 2012 and has since gained popularity for its ability to streamline the development process and reduce boilerplate code.
  • Spring Data MongoDB: Spring Data MongoDB is another member of the Spring Data family and offers seamless integration with the MongoDB NoSQL database. It provides a high-level abstraction for working with MongoDB, allowing developers to easily perform CRUD operations, define queries, and leverage MongoDB-specific features. Spring Data MongoDB was introduced in 2010 and has been widely adopted in projects that utilize MongoDB as their database backend.
  • Spring Data Redis: Spring Data Redis is a powerful tool for integrating Redis, an in-memory data structure store, with Spring applications. It provides a simplified programming model for interacting with Redis, enabling developers to seamlessly leverage Redis features such as caching, pub/sub messaging, and distributed data structures. Spring Data Redis has been actively developed since 2012 and is widely used in applications that require high-performance data storage and retrieval.
  • Spring Data JDBC: Spring Data JDBC is a lightweight alternative to traditional ORM frameworks like Hibernate. It provides a simple and efficient way to interact with relational databases using plain SQL queries and minimal configuration. Spring Data JDBC was introduced in 2018 and has gained traction in projects that prioritize performance and simplicity over advanced ORM capabilities.
  • Spring Data Elasticsearch: Spring Data Elasticsearch is a specialized module for integrating Elasticsearch, a distributed search and analytics engine, with Spring applications. It offers a convenient way to interact with Elasticsearch through a high-level API, enabling developers to perform full-text search, aggregations, and other advanced operations. Spring Data Elasticsearch was first released in 2013 and has become the go-to choice for projects that require powerful search capabilities.
  • Spring Data Neo4j: Spring Data Neo4j provides seamless integration with Neo4j, a popular graph database. It offers an object-graph mapping framework that allows developers to work with graph data using a familiar domain model. Spring Data Neo4j was introduced in 2010 and has been widely adopted in applications that leverage the power of graph databases for complex data relationships and network analysis.
  • Spring Data Couchbase: Spring Data Couchbase allows developers to easily integrate Couchbase, a NoSQL document database, with Spring applications. It provides a high-level abstraction for working with Couchbase, enabling developers to perform CRUD operations, define queries, and leverage Couchbase-specific features. Spring Data Couchbase has been actively developed since 2012 and is a popular choice for projects that require flexible and scalable document storage.

How and where is Spring Data used?

Case NameCase Description
Case 1: Simplified Database AccessSpring Data Development provides a simplified and consistent approach to accessing databases. It offers convenient abstractions and APIs to interact with various database technologies, including relational databases, NoSQL databases, and even graph databases. With Spring Data, developers can easily perform CRUD operations, execute complex queries, and handle transactions without writing boilerplate code.
Case 2: JPA IntegrationSpring Data JPA, a subproject of Spring Data, integrates seamlessly with the Java Persistence API (JPA). It eliminates the need for repetitive and verbose JPA code by providing automatic repository implementation based on conventions. Developers can define and customize repositories using simple interfaces and query methods, allowing for efficient data access and manipulation.
Case 3: MongoDB IntegrationSpring Data MongoDB enables developers to work with MongoDB, a popular NoSQL database, using a familiar programming model. It supports advanced querying, indexing, and aggregation operations, making it easier to build scalable and high-performance applications. By leveraging Spring Data’s features, developers can seamlessly integrate MongoDB into their Spring projects without dealing with low-level details.
Case 4: Redis IntegrationSpring Data Redis offers a comprehensive integration with Redis, an in-memory data structure store. It provides support for key-value operations, publish-subscribe messaging, and distributed locking, making it an excellent choice for building real-time applications, caching layers, and session management systems. Spring Data simplifies the configuration and usage of Redis within Spring applications, allowing developers to focus on business logic.
Case 5: Custom Data Store IntegrationSpring Data Development allows developers to integrate with custom data stores that are not directly supported out-of-the-box. By implementing the necessary interfaces and providing appropriate configurations, developers can seamlessly connect their Spring applications with any data store, extending the capabilities of Spring Data to support diverse and specialized databases.
Case 6: Pagination and SortingSpring Data provides built-in support for pagination and sorting of query results. Developers can easily define pageable queries using method signatures or annotations, enabling efficient navigation through large datasets. Spring Data takes care of generating the necessary SQL or NoSQL queries and handles the pagination and sorting logic transparently.
Case 7: Auditing and VersioningSpring Data incorporates auditing and versioning features to track changes and manage historical data in a database. With annotations and configuration options, developers can automatically capture information about entity modifications, such as creation date, last modification date, and user responsible for the change. This enables audit trails, historical analysis, and data integrity verification.
Case 8: Cross-Store PersistenceSpring Data supports cross-store persistence, allowing developers to combine different data stores within a single application. This feature is particularly useful when dealing with hybrid environments that require data to be stored in multiple databases or systems. Spring Data provides the necessary abstractions and mechanisms to seamlessly integrate and manage data across diverse stores.
Case 9: Reactive Data AccessSpring Data offers reactive support for data access using the Reactive Streams API and reactive programming paradigms. Developers can build responsive and scalable applications by leveraging asynchronous and non-blocking data access operations. Spring Data supports reactive database technologies, such as MongoDB, Cassandra, and Redis, enabling the development of highly performant and resilient systems.

TOP 10 Spring Data Related Technologies

  • Java

    Java is the most popular programming language for Spring Data software development. It offers a robust and reliable platform for building enterprise-grade applications. With its extensive libraries and frameworks, Java provides developers with the tools they need to create scalable and efficient data-driven solutions.

  • Spring Framework

    The Spring Framework is a widely adopted framework for building Java applications. It provides a comprehensive set of features and modules that simplify the development process. Spring Data, a part of the Spring Framework, offers convenient abstractions and APIs for working with various data sources, making it an essential tool for Spring Data software development.

  • SQL

    SQL (Structured Query Language) is a standard language for managing relational databases. It is a fundamental skill for developers working with Spring Data, as it allows them to interact with databases and perform operations such as querying, inserting, updating, and deleting data.

  • JavaScript

    JavaScript is a versatile programming language that is widely used in web development. It plays a crucial role in Spring Data software development, especially when building user interfaces and implementing client-side logic. JavaScript frameworks like React and AngularJS are popular choices for developing interactive and responsive web applications.

  • NoSQL

    NoSQL databases provide an alternative to traditional relational databases, allowing for more flexible and scalable data storage. MongoDB, Cassandra, and Redis are popular NoSQL databases often used in Spring Data software development, enabling developers to handle large volumes of data and achieve high performance.

  • Git

    Git is a distributed version control system widely used in software development. It allows developers to track changes, collaborate with team members, and manage code repositories efficiently. Git is an essential tool for Spring Data software development, enabling seamless collaboration and version control.

  • RESTful APIs

    REST (Representational State Transfer) is an architectural style for designing networked applications. RESTful APIs provide a standardized way of accessing and manipulating data over the web. They are commonly used in Spring Data software development to create scalable and interoperable systems.

Cases when Spring Data does not work

  1. Unsupported databases: Spring Data works seamlessly with a wide range of databases, including popular ones like MySQL, PostgreSQL, and MongoDB. However, there might be cases where the database you are using is not supported by Spring Data. In such cases, you won’t be able to leverage the benefits of Spring Data’s automatic repository implementations, query generation, and other features.
  2. Complex queries: While Spring Data provides a convenient way to generate queries based on method names, it might not be suitable for complex queries involving multiple joins, subqueries, or intricate conditions. In such scenarios, you may need to resort to writing native SQL queries or using other query frameworks.
  3. Custom data access logic: Spring Data simplifies common CRUD operations by providing generic repository interfaces. However, if you need to implement custom data access logic that goes beyond the basic CRUD operations, you might find limitations with Spring Data. In such cases, you may need to resort to writing your own data access layer using JDBC or other frameworks.
  4. Data model inconsistencies: Spring Data relies on a consistent data model to generate queries and perform CRUD operations. If your data model is inconsistent or does not adhere to the expected conventions, Spring Data may encounter difficulties in mapping entities and executing operations correctly.
  5. Performance optimization: While Spring Data offers convenient abstractions for data access, it may not always provide the most optimal performance for your specific use case. In situations where fine-grained control over data access and performance optimization is crucial, you might need to consider alternative approaches like using native queries, caching mechanisms, or implementing custom data access logic.

Soft skills of a Spring Data Developer

Soft skills are essential for a Spring Data Developer as they contribute to effective communication, collaboration, and overall success in a development team. Here are the soft skills required at different levels of expertise:

Junior

  • Active Listening: Ability to listen and understand requirements, issues, and concerns effectively.
  • Problem Solving: Capacity to analyze and resolve issues by thinking critically and proposing solutions.
  • Adaptability: Willingness to learn and adapt to new technologies and methodologies.
  • Teamwork: Capability to work collaboratively within a team to achieve common goals.
  • Time Management: Skill to manage tasks and prioritize effectively to meet deadlines.

Middle

  • Communication: Proficiency in expressing ideas, concepts, and technical information clearly and concisely.
  • Leadership: Capability to guide and mentor junior developers, facilitating their growth.
  • Collaboration: Ability to work closely with other teams and stakeholders, fostering a cooperative environment.
  • Problem Solving: Experience in identifying complex issues and providing innovative solutions.
  • Decision Making: Skill to make informed decisions based on available information and critical analysis.
  • Adaptability: Flexibility to adapt to changing project requirements and technologies.
  • Conflict Management: Ability to identify and resolve conflicts within the team or with stakeholders.

Senior

  • Strategic Thinking: Capability to align development efforts with long-term business objectives.
  • Mentorship: Experience in guiding and mentoring developers at various levels.
  • Project Management: Proficiency in managing project scope, timelines, and resources.
  • Influence: Ability to influence and persuade stakeholders to adopt best practices and solutions.
  • Empathy: Skill to understand and empathize with team members, fostering a positive work environment.
  • Critical Thinking: Capacity to analyze complex problems and make sound decisions.
  • Continuous Learning: Commitment to staying updated with the latest trends and technologies.
  • Conflict Resolution: Expertise in resolving conflicts and promoting harmony within the team.

Expert/Team Lead

  • Visionary Leadership: Ability to articulate a vision and inspire the team to achieve it.
  • Strategic Planning: Proficiency in developing long-term development strategies and roadmaps.
  • Cross-functional Collaboration: Skill to collaborate with multiple teams and stakeholders to deliver complex projects.
  • Innovation: Capacity to drive innovation and introduce new technologies and approaches.
  • Decision Making: Expertise in making critical decisions that impact the overall success of the project.
  • Conflict Resolution: Ability to handle conflicts and challenges at a higher organizational level.
  • Change Management: Experience in managing and mitigating risks associated with changes in project scope or requirements.
  • Client Management: Skill to manage client expectations and build strong relationships.
  • Technical Expertise: Deep understanding and proficiency in Spring Data and related technologies.
  • Team Building: Capability to build and nurture high-performing development teams.
  • Communication: Excellent communication skills to effectively convey complex technical concepts to both technical and non-technical stakeholders.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior0-2 yearsAssisting in development tasks, bug fixing, code reviews, and learning from senior team members. Working on smaller projects or specific features under supervision.$50,000 – $70,000
Middle3-5 yearsDeveloping and implementing features, modules, or components independently. Collaborating with team members and providing guidance to junior developers. Participating in code reviews and ensuring adherence to coding standards. Working on medium-sized projects or complex features.$70,000 – $90,000
Senior6-10 yearsLeading the development of major features, modules, or applications. Mentoring and guiding junior and middle developers. Participating in architectural discussions and making technical decisions. Ensuring high-quality code through code reviews and testing. Working on large-scale projects or critical components.$90,000 – $120,000
Expert/Team Lead10+ yearsLeading a team of developers, coordinating project activities, and managing timelines. Making strategic technical decisions and providing guidance on architectural design. Mentoring and coaching team members, conducting performance evaluations. Collaborating with stakeholders and other teams. Responsible for delivering high-quality products on time and within budget.$120,000 – $150,000+

TOP 12 Tech facts and history of creation and versions about Spring Data Development

  • Spring Data is an open-source project that provides a consistent programming model for data access across different data sources.
  • It was created in 2009 by Oliver Gierke, a software engineer at Pivotal Software (now part of VMware).
  • Spring Data aims to simplify the development of data-driven applications by abstracting away the complexities of interacting with various databases and data stores.
  • The first version of Spring Data, known as Spring Data JPA, focused on providing a simplified API for working with Java Persistence API (JPA) implementations.
  • Since its inception, Spring Data has evolved to support a wide range of data sources, including relational databases, NoSQL databases, and even cloud-based storage services.
  • Spring Data incorporates various methodologies and design patterns, such as the Repository pattern, to provide a consistent and intuitive way of working with data.
  • One of the groundbreaking features of Spring Data is its ability to generate repository implementations at runtime based on the defined interfaces.
  • Spring Data also provides support for query creation based on method names, allowing developers to write expressive queries without explicitly writing SQL or other query languages.
  • Over the years, Spring Data has expanded to include modules for specific data technologies, such as Spring Data MongoDB, Spring Data Redis, and Spring Data Elasticsearch.
  • Spring Data has a vibrant community and is actively maintained and improved by a dedicated team of developers.
  • The latest version of Spring Data, as of writing, is Spring Data 2021.0, which includes updates and enhancements for various modules.
  • Spring Data is widely used in enterprise-level applications and has contributed to the overall productivity and efficiency of developers working with data access in Java.

TOP 12 Facts about Spring Data

  • Spring Data is an umbrella project of the Spring Framework that provides easy and efficient integration with various data access technologies.
  • It follows the principle of convention over configuration, reducing the amount of boilerplate code required for data access.
  • Spring Data offers a consistent programming model across different data stores, including relational databases, NoSQL databases, and distributed data grids.
  • It supports a wide range of data access technologies, such as JDBC, JPA (Java Persistence API), MongoDB, Elasticsearch, Cassandra, Redis, and many more.
  • Spring Data provides repositories, which are interfaces that define high-level data access operations, allowing developers to interact with the database using a simple and intuitive API.
  • By leveraging Spring Data repositories, developers can perform common CRUD (Create, Read, Update, Delete) operations without writing explicit SQL or NoSQL queries.
  • It offers powerful query methods derived from method names, allowing developers to express complex queries using a concise and readable syntax.
  • Spring Data supports pagination and sorting out-of-the-box, making it easy to retrieve large datasets in a controlled and efficient manner.
  • It integrates seamlessly with the Spring Framework, leveraging its features such as dependency injection, transaction management, and declarative caching.
  • Spring Data provides support for auditing, allowing developers to automatically track and log changes to entities, including creation and modification timestamps, as well as the responsible user.
  • It offers support for reactive programming models, enabling developers to build scalable and responsive applications using technologies like Spring WebFlux and Project Reactor.
  • Spring Data has a vibrant and active community, with regular updates and improvements, ensuring that developers have access to the latest features and bug fixes.

Pros & cons of Spring Data

8 Pros of Spring Data

  • 1. Simplified Data Access: Spring Data provides a simplified and consistent approach to accessing various data sources such as relational databases, NoSQL databases, and cloud-based data stores. It abstracts the complexities of data access, allowing developers to focus on business logic rather than dealing with low-level details.
  • 2. Increased Productivity: By leveraging Spring Data’s powerful features such as automatic CRUD operations, query generation, and pagination support, developers can write less boilerplate code and achieve faster development cycles. This increased productivity can lead to reduced time-to-market for applications.
  • 3. Seamless Integration with Spring Ecosystem: Spring Data seamlessly integrates with the larger Spring ecosystem, including frameworks like Spring MVC and Spring Boot. This integration makes it easier to build end-to-end applications using a unified and consistent programming model.
  • 4. Flexible Querying: Spring Data provides a flexible and expressive querying mechanism that allows developers to define queries using method names, annotations, or even custom query objects. This flexibility enables efficient and optimized data retrieval, reducing the need for manual query construction.
  • 5. Support for Multiple Data Sources: With Spring Data, developers can easily switch between different data sources without rewriting application code. It supports a wide range of database technologies, making it suitable for projects that require multi-database or multi-data-source support.
  • 6. Integration with Caching: Spring Data seamlessly integrates with popular caching frameworks such as Redis and Ehcache, allowing developers to improve application performance by caching frequently accessed data. This integration helps reduce the load on the underlying data store and improves response times.
  • 7. Transparent Transaction Management: Spring Data provides transparent transaction management capabilities, allowing developers to handle database transactions without dealing with low-level transaction APIs. This simplifies the process of managing transactions and ensures data integrity in multi-threaded or distributed environments.
  • 8. Active Community Support: Spring Data has a large and active community of developers who contribute to its development and provide support through forums, documentation, and open-source contributions. This active community ensures that Spring Data remains up-to-date, reliable, and well-maintained.

8 Cons of Spring Data

  • 1. Learning Curve: As with any complex framework, there is a learning curve associated with Spring Data. Developers need to invest time in understanding its concepts, APIs, and best practices to effectively utilize its features. However, the comprehensive documentation and community support can help mitigate this challenge.
  • 2. Lack of Fine-grained Control: While Spring Data provides convenience and abstraction, it may limit fine-grained control over certain aspects of data access. In scenarios where specific optimizations or customizations are required, developers may need to resort to lower-level data access frameworks or APIs.
  • 3. Performance Overhead: Although Spring Data offers powerful features, there can be a slight performance overhead compared to manual data access implementations. This overhead is primarily due to the abstraction and additional layers of code generated by Spring Data. However, the impact on performance is often negligible and can be outweighed by the productivity gains.
  • 4. Limited Support for Complex Queries: While Spring Data offers flexible querying capabilities, it may have limitations when it comes to handling complex or dynamic queries involving multiple joins, aggregations, or nested conditions. In such cases, developers may need to resort to native SQL or alternative query languages.
  • 5. Dependency Management: Spring Data has dependencies on other Spring modules and libraries, which may introduce additional complexity in terms of version compatibility and dependency management. Careful consideration needs to be given to ensure all dependencies are properly managed to avoid conflicts or compatibility issues.
  • 6. Lack of Full Control with NoSQL Databases: Although Spring Data provides support for NoSQL databases, it may not offer full control over all the features and capabilities of each specific NoSQL database. Developers may need to resort to database-specific APIs or frameworks for advanced functionality.
  • 7. Limited Support for Legacy Systems: Spring Data primarily focuses on modern data access technologies and may have limited support for legacy systems or older database versions. This can be a limitation for projects that need to integrate with legacy data sources or have specific compatibility requirements.
  • 8. Complexity in Custom Implementations: While Spring Data simplifies common data access scenarios, implementing custom data access logic or complex data mappings may require additional effort and expertise. Developers need to have a strong understanding of Spring Data’s internal mechanisms to achieve desired customizations.

Hard skills of a Spring Data Developer

As a Spring Data Developer, you need to possess a set of hard skills to excel in your role. These skills vary depending on your level of experience, whether you are a Junior, Middle, Senior, or an Expert/Team Lead. Let’s take a look at the hard skills required at each level:

Junior

  • Spring Framework: Familiarity with the core concepts and features of the Spring Framework.
  • Java Development: Proficiency in Java programming language and its related technologies.
  • Database Knowledge: Understanding of databases and proficiency in SQL.
  • Spring Data JPA: Knowledge of Spring Data JPA, which provides an abstraction layer for interacting with relational databases.
  • Version Control: Experience with version control systems like Git for source code management.

Middle

  • Advanced Spring Concepts: In-depth understanding of advanced Spring concepts like Spring Boot, Spring Security, and Spring MVC.
  • Database Modeling: Proficiency in database modeling and design principles.
  • Query Optimization: Ability to optimize database queries for improved performance.
  • Testing: Knowledge of unit testing and integration testing frameworks like JUnit and Mockito.
  • RESTful APIs: Experience in building RESTful APIs using Spring frameworks.
  • Continuous Integration: Familiarity with continuous integration tools like Jenkins or Travis CI.
  • Performance Tuning: Ability to identify and resolve performance issues in Spring Data applications.

Senior

  • Microservices Architecture: Proficiency in building and scaling microservices using Spring Cloud.
  • NoSQL Databases: Experience with NoSQL databases like MongoDB or Cassandra.
  • Containerization: Knowledge of containerization technologies like Docker and Kubernetes.
  • Security: Understanding of security best practices and experience in implementing secure applications.
  • Code Review: Ability to conduct code reviews and provide constructive feedback to team members.
  • Design Patterns: Proficiency in various design patterns and their application in Spring Data development.
  • Performance Monitoring: Experience with performance monitoring and profiling tools like JProfiler or New Relic.
  • Agile Methodology: Familiarity with agile development methodologies like Scrum or Kanban.

Expert/Team Lead

  • Architectural Design: Ability to design complex and scalable architectures for Spring Data applications.
  • Leadership: Strong leadership skills to guide and mentor a team of developers.
  • Code Quality: Expertise in maintaining code quality through code reviews, refactoring, and best practices.
  • Cloud Technologies: Proficiency in deploying and managing Spring Data applications on cloud platforms like AWS or Azure.
  • Performance Optimization: Deep understanding of performance optimization techniques and tools for Spring Data applications.
  • Database Replication: Experience with database replication and clustering for high availability.
  • Big Data Integration: Knowledge of integrating Spring Data applications with big data technologies like Apache Hadoop or Apache Spark.
  • Project Management: Experience in project management, including planning, estimation, and resource allocation.
  • Team Collaboration: Ability to collaborate effectively with cross-functional teams and stakeholders.
  • Problem Solving: Strong problem-solving skills to tackle complex technical challenges.
  • Continuous Learning: Commitment to continuous learning and staying updated with the latest developments in Spring Data and related technologies.

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Manager
Maria Lapko
Global Partnership Manager