Position Overview This role is remote within Canada. Candidates must be Canadian citizens or permanent residents currently residing in Canada. Applications from outside Canada will not be considered. Responsibilities Architect and own the spatial data platform that every Atreides mapping and visualization view reads from—Apache Iceberg with native geospatial functionality, Apache Sedona for spatial processing, Apache Arrow for columnar interchange, Python and Spark SQL for the transformation surface. Design and build the tile generation and serving infrastructure (Martin, PMTiles, Mapbox Vector Tiles, tippecanoe) that lets Atreides deliver sub‑second map updates over petabyte‑scale archives. Choose and operate the analytical engines that serve interactive visualization workloads—DuckDB for the latency‑sensitive client‑adjacent layer, StarRocks for the operator‑facing analytical layer, Spark SQL for the heavy transformations behind both. Defend why each lives where it lives. Define the spatial indexing strategy (H3, S2, quadtree, plus PostGIS where it makes sense) and the partitioning schemes that hold up under the workloads Atreides actually runs—not the ones we modelled on paper. Own the freshness, cost, and latency budget on the data side: ingestion throughput, tile‑build wall time, analytical query p95, cache hit rates, and the API contracts that the frontend Mapping and Visualization Engineers depend on. The frontend cannot be fast if the backend serves the wrong shapes. Partner closely with the Senior Mapping Engineer and Visualization Engineers to design the data shapes, query patterns, and API contracts that let the visualization layer hit its frame rate and latency targets. Establish engineering best practices for the data platform—workflow orchestration (Airflow, Dagster, Temporal), CI/CD, performance profiling on the data tier, observability, schema governance, and release safety. Lead the integration of open geospatial ecosystems (OpenStreetMap, Overture Maps Foundation) alongside commercial, sovereign, and customer‑provided feeds, and bring a clear point of view on when each source belongs in the platform. Thoughtfully integrate AI into your design, development, and decision‑making; our interview process will include a discussion of how you use AI in your engineering workflow. Mentor engineers across the platform—both data‑side peers and frontend‑side partners—and contribute to the hiring and growth of the Insights Platform team. Author and review technical design documents for data platform initiatives, architecture changes, and new services. Desired Qualifications 7+ years of professional backend software engineering experience, with at least 3 years focused on geospatial data platforms, large‑scale spatial data processing, or the data infrastructure behind interactive visualization products. Deep expertise in the Atreides data stack: Python and Spark SQL for the transformation surface, Apache Sedona for spatial processing, Apache Iceberg for the lakehouse, Apache Arrow for columnar interchange. Demonstrated expertise across at least three of: analytical engines at scale (DuckDB, StarRocks, Spark SQL); spatial indexing strategies (H3, S2, quadtree, PostGIS); tile generation and serving (Martin, PMTiles, Mapbox Vector Tiles, tippecanoe); workflow orchestration and data freshness (Airflow, Dagster, Temporal); cloud‑native data infrastructure (AWS, GCP, Azure with Kubernetes and IaC); or large‑scale streaming and CDC (Kafka, Kinesis, Flink, Debezium). Strong, defended opinions on the trade‑offs that shape spatial data platform performance—when to pre‑aggregate into vector tiles versus compute live, when an H3 index is overkill and when it is not enough, when to materialize versus when to query, when freshness justifies recompute cost, when caching belongs at the tile layer versus the query layer. Deep fluency with geospatial fundamentals—coordinate systems, projections, spatial indexing, common formats (GeoJSON, GeoParquet, COG, Shapefile, KML, vector tiles, PMTiles), and OGC standards. Track record of architecting and shipping production‑grade data platforms that run at scale and in high‑availability environments. Working familiarity with the frontend stack the platform serves—MapLibre GL JS, Mapbox GL JS, deck.gl—and an active interest in the contract between backend and visualization. You do not need to ship frontend code, but you should understand what the frontend is actually doing with the data you give it. Strong communicator, comfortable writing technical designs, reviewing code across specialties, and speaking directly with frontend partners and customer technical authorities. Curiosity about open geospatial ecosystems (OpenStreetMap, Overture Maps Foundation, OpenAddresses) and a clear point of view on the trade‑offs between open, commercial, and sovereign data in a defence context. A thoughtful approach to using AI in your engineering workflow; we will discuss how and when , you integrate AI into design, development, and decision‑making. Bonus: open‑source contributions to a spatial data platform project (Apache Sedona, Apache Iceberg, GeoParquet, H3, Martin, PMTiles, DuckDB spatial, GDAL, etc.); prior defence, intelligence, or public‑safety experience, or any existing Canadian security clearance Compensation and Benefits Comprehensive health, dental, and vision insurance plans Flexible remote work environment Additionalbenefits like flexible hours, work travel opportunities, competitive vacationtimeand parental leave #J-18808-Ljbffr