Mapbox
Software Development Engineer II, Search Data
About this role
Join Mapbox's Search Data team to build and maintain large-scale ETL pipelines that process hundreds of millions of geospatial records daily. You'll design batch and streaming data ingestion systems on AWS, collaborate across teams to deliver address and POI data for global search, and mentor junior engineers while maintaining operational excellence.
What you'll do
- Build and support batch and streaming ingestion systems processing terabytes of data daily
- Design and optimize ETL pipelines for addresses, road networks, buildings, and POI data
- Partner with cross-functional teams to understand data requirements and deliver solutions
- Simplify deployment, monitoring, and management of data workloads on AWS
- Mentor engineers and lead code/design reviews while reducing technical debt
- Ensure system reliability through testing, monitoring, documentation, and on-call support
What they're looking for
- Backend system and data pipeline design (5+ years)
- AWS services (Lambda, S3, Athena, Glue, EMR)
- Python and SQL
- Modern backend language (NodeJS, Scala, or Java)
- Apache Spark or Hadoop-based technologies
- CI/CD processes
- Data quality and operational metrics
- API integration and querying
Benefits
- Work with geospatial and telemetry data at massive scale
- Comprehensive health care and parental leave
- Flexible work arrangements
- Mentorship and professional development opportunities
- Collaborative, inclusive engineering culture
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Mapbox
Mapbox builds geospatial data infrastructure and location-based services including search, geocoding, offline navigation, and traffic systems that power real-time location experiences across mobile and automotive platforms. The company is hiring Software Development Engineers II and data engineers to build large-scale ETL pipelines, optimize search algorithms, develop navigation services, and create reliable web infrastructure while leveraging AI technologies.
- Website
- mapbox.com
Likely interview questions
- Describe a complex ETL pipeline you designed—what were the scaling challenges and how did you solve them?
- How have you used AWS services like Spark, EMR, or Glue to process large datasets, and what trade-offs did you consider?