Our client is a is a location data and technology platform company that empower customers to achieve better outcomes - from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely. They create solutions that fuel innovation, provide opportunity and foster inclusion to improve people's lives. If you are inspired by an open world and driven to create positive change, join us! As a Backened Engineer in the Map Data Processing group you will develop smart Map Data processing tools for the state-of-the-art Mapping Technologies. You will work self-sustained in an agile team. Your responsibility will cover developing, extending and maintaining tool and services that processes map data for global navigational database. For that you will be translating product strategies into technology strategies, leading the long-term architectural direction and helping design and build industry-grade customer-facing geo-data-intensive products. You will work closely with other engineering & operations teams, internal users of tool chains your team develops and partner with product managers and the larger map operations business units.Project overview: Join our engineering organization to build the next generation automated core map data processing environment for a global provider of location and mapping solutions used in automotive and mobility. The product focus is on developing intelligent source-to-observables systems: scalable backend services and big data pipelines that transform diverse raw inputs ("sources") into reliable, measurable outputs ("observables") such as quality signals, coverage indicators, change detection outputs, operational KPIs, and downstream-ready datasets/artifacts. Working as part of an agile team, you will contribute to systems that improve safety, availability, and performance of large-scale data processing that supports next-gen navigation and automated driving capabilities.What you'll work on Distributed processing pipelines for large-scale datasets (batch and, where applicable, near-real-time) Data validation, quality gates, and traceability concepts Cloud-native execution environments (infrastructure automation, CI/CD, operational readiness)Technologies : Java Scala (optional) Apache Spark AWS (incl. EMR and related services) CI/CD tooling (e.g., GitLab CI or similar)Requirements: 7+ years (or strong equivalent) building backend systems with Java (strong core Java) Strong knowledge of Core Java and object-oriented programming principles Good understanding of concurrency and multithreading concepts Solid knowledge of data structures (Lists, Trees, Graphs, Stacks, Queues) and ability to implement them Solid knowledge of Algorithms(Big O notation, time/space complexity, search, sort, etc.) Experience with distributed data processing (Apache Spark is a plus) Strong experience with relational databases and SQL (PostgreSQL preferred) Understanding of JVM fundamentals and performance optimisation Practical experience with AWS (EMR or similar managed compute services is a plus) Experience working with CI/CD pipelines and engineering best practices (code reviews, testing, automation) Strong analytical and debugging skills with a focus on performance and reliability English level: Upper-Intermediate or higher Nice to have Scala (optional, but a plus) Experience with data transformation / ETL pipelines Experience with geospatial/map-related data or large-scale data quality systemsResponsibilities: Design and implement scalable backend services and data processing pipelines Participate in architecture/design discussions and contribute to technical decisions Own delivery end-to-end: development, testing, performance optimisation, and production support readiness Improve code quality via reviews, automated testing, and engineering best practices Contribute to estimations, planning, and iterative delivery in an Agile/Scrum process Mentor engineers, share knowledge, and promote a strong engineering culture.