Staff Geospatial Engineer

We’re looking for a Staff-level Geospatial Engineer to own and evolve our geospatial data platform—from raw imagery ingest to near real-time delivery of high-quality datasets used in production ML systems. This role sits at the intersection of geospatial systems, data engineering, and ML infrastructure, with a strong emphasis on treating datasets as first-class products .

You’ll lead the design of scalable pipelines, define best practices for dataset lifecycle management, and partner closely with ML, infra, and product teams to ensure data reliability, lineage, and observability at scale.

What You’ll Do

• Design and operate large-scale imagery ingest, normalization, and cataloging pipelines across diverse sources.
• Build and optimize tiling/chipping pipelines to support fast spatial queries and near real-time data delivery .
• Own dataset and label versioning systems , including QA workflows that ensure data correctness, reproducibility, and auditability.
• Integrate dataset and version lineage with artifact stores and registries (experience with W&B Artifacts is a strong plus).
• Treat datasets as products :
  • Define and maintain APIs for dataset access
  • Establish SLAs around freshness, availability, and quality
  • Handle backfills and reprocessing gracefully
  • Implement observability, metrics, and alerting for data pipelines
• Drive architectural decisions and technical direction for geospatial data systems.
• Mentor engineers and raise the bar for data engineering and geospatial best practices across the org.

What We’re Looking For

• 5+ years of professional experience building data or geospatial systems in production environments.
• Deep experience with geospatial imagery pipelines (satellite, aerial, or similar large raster datasets).
• Strong understanding of tiling schemes, spatial indexing, and high-performance retrieval .
• Hands‑on experience with dataset and label versioning , including QA and validation workflows.
• Familiarity with artifact stores / registries and dataset lineage tracking (W&B Artifacts preferred).
• A pragmatic, product-oriented mindset toward data: reliability, usability, and operability matter as much as correctness.
• Comfort operating at Staff level: ambiguous problems, cross-team influence, and long-term technical ownership.

Nice to Have

• Experience supporting ML training and evaluation pipelines at scale.
• Background in remote sensing, mapping, or geospatial analytics.
• Experience designing data SLAs and operational playbooks.
• Prior ownership of data platforms used by multiple teams or customers.

Why This Role

• High ownership over core geospatial and ML data infrastructure.
• Direct impact on production systems and downstream models.
• Fully remote with a team that values autonomy, clarity, and strong engineering fundamentals.