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Backend Scala Developers in Spain

Led the development of a scalable microservices architecture in Java and Spring for e-commerce platforms. Architected NoSQL database solutions to support high-volume transactional systems.

• Developed scalable microservices architecture
• Architected NoSQL database solutions
• Supported high-volume transactional systems

Designed microservices-based architectures using Scala and Akka. Extensive experience in building scalable backend solutions with Spring Boot.

• Designed microservices-based architectures
• Experienced with Spring Boot
• Built scalable backend solutions

Led development of high-performance backend services for e-commerce platforms, focusing on scalability and reliability. Engineered data-intensive applications with SQL Server, optimizing database interactions for complex queries.

• Built RESTful APIs for large-scale web applications
• Enhanced legacy systems with modern programming paradigms
• Implemented cross-platform solutions using C# and Rust

Built scalable microservices architectures deployed on Kubernetes, optimizing for high availability and fault tolerance. Developed distributed systems for real-time data processing in the IoT domain, leveraging Go and Scala. Spearheaded efforts in implementing CI/CD pipelines for seamless deployment and integration.

• Implemented event-driven architecture using Kafka and RabbitMQ
• Enhanced application performance through advanced caching strategies
• Designed RESTful APIs with comprehensive testing frameworks

Designed and implemented a large-scale event-driven architecture for IoT data ingestion using Scala and Akka. Specialized in building concurrency models with Rust to support multi-threaded processing in high-load environments. Spearheaded the development of a real-time monitoring system for SQL databases, enhancing query performance by 30%.

• Developed a distributed logging system using Actix and Rust.
• Integrated TypeScript-based UI with backend services for seamless data flow.
• Led migration of legacy systems to cloud infrastructure, improving resilience.

Pioneered distributed systems architecture, focusing on Kubernetes orchestration and Rust for high-performance computing. Led initiatives in developing and scaling cloud-native applications with extensive use of Scala and Python.

• Developed custom Kubernetes operators
• Optimized JVM performance for large-scale applications
• Engineered real-time analytics platforms using JavaScript

Designed and implemented distributed data processing systems using the Apache Spark framework, optimizing performance for large-scale data transformations. Developed real-time event-driven applications leveraging the Pekko/Akka ecosystem to enhance responsiveness and scalability in high-throughput environments.

• Built ETL pipelines for financial data analysis
• Integrated Scala with legacy Java systems for seamless interoperability
• Optimized SQL queries for improved database performance
• Implemented functional programming principles to reduce system complexity

Built a full-stack web application using Scala and TypeScript, focusing on seamless user experience. Developed a real-time notification system using Rust for high-frequency trading platforms. Created a data visualization dashboard with Haskell for complex datasets.

• Integrated microservices with message brokers for asynchronous processing
• Utilized Python for backend data processing tasks
• Implemented end-to-end testing frameworks for web applications

Designed and optimized large-scale distributed data processing systems using Apache Spark, focusing on performance enhancements for data-intensive applications. Developed custom Spark transformations and actions, significantly reducing execution time for ETL pipelines in the financial analytics domain. Integrated Rust components with Scala to improve computational efficiency in real-time data processing.

• Implemented advanced caching strategies for Spark job optimization.
• Contributed to open-source Apache DataFusion project with Rust.
• Developed PySpark solutions for streaming data pipelines.
• Led migration of legacy data systems to cloud-based infrastructure.

Engineered large-scale data processing workflows with Apache Spark, optimizing ETL operations. Developed data warehousing solutions using Hive for efficient querying and reporting. Managed Hadoop clusters, ensuring high availability and fault tolerance.

• Implemented real-time analytics using Spark Streaming
• Enhanced SQL query performance with custom indexing
• Automated data pipeline monitoring and alerts