Geospatial Data Engineer

Basepoint; San Francisco, Northern (CA, KY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Basepoint is seeking a Data Engineer to own ingestion, normalization, tiling, and refresh of national geospatial data layers. You will build pipelines for federal, state, utility, and commercial sources into PostGIS while normalizing diverse schemas and documenting field provenance.

You will generate vector tiles and server-side queries, design integrity checks, and assume runbooks when data sources shift. Strong SQL, PostGIS, and Python/TypeScript are required, with tiling experience a must.

Qualifications

• Four or more years in data engineering with substantial geospatial work.
• Strong SQL and PostGIS, plus Python or TypeScript for pipeline code.
• Experience with tiling (PMTiles, MVT, or similar), coordinate reference systems, and large raster or vector datasets.

Responsibilities

• Own the pipelines that ingest, normalize, tile, and refresh geospatial layers.
• Build ingestion pipelines for federal, state, utility, and commercial sources into PostGIS.
• Normalize heterogeneous schemas into stable internal models and document provenance.
• Generate and serve vector tiles and server-side screening queries at national scale.
• Design freshness and integrity checks and maintain runbooks when sources change.

Job description

Basepoint serves dozens of national data layers: parcels, transmission and substations, hosting capacity, wetlands, floodplains, habitat, zoning, and queue data. Every one of them comes from a different source with its own refresh cadence, schema, and quirks.

You will own the pipelines that ingest, normalize, tile, and refresh those layers, and the quality checks that catch a bad feed before a customer does.

Build ingestion pipelines for federal, state, utility, and commercial geospatial sources into PostGIS.

Normalize heterogeneous schemas into stable internal models and document provenance for every field.

Generate and serve vector tiles and server-side screening queries that stay fast at national scale.

Design freshness and integrity checks, and own the runbooks when a source changes shape or goes dark.

Partner with product engineers on new layers and with domain experts on what the data actually means.

Four or more years in data engineering with substantial geospatial work.

Strong SQL and PostGIS, plus Python or TypeScript for pipeline code.

Experience with tiling (PMTiles, MVT, or similar), coordinate reference systems, and large raster or vector datasets.

A bias toward observable, idempotent pipelines over clever one-offs.