Requirement:
- 5+ years (or strong equivalent) building backend systems with Java (strong core Java)
- Solid knowledge of data structures, OOP, and design patterns
- Experience with Big Data / distributed processing, ideally Apache Spark
- Strong experience with relational databases and SQL, ideally PostgreSQL (schema design, query optimization, indexes
- Practical experience in AWS, preferably including EMR or equivalent managed compute
- Experience with CI/CD and engineering best practices (code reviews, testing, automation)
- Strong analytical and debugging skills; ability to optimize performance and reliability
- English communication skills Upper Intermediate + (written and spoken)
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 systems
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)
