Spotable builds the AI platform that lets roofing and façade contractors measure buildings in 3D, generate quantities and create winning quotations in minutes.

Behind that experience sits a massive geospatial engine. We ingest aerial data, cadastral data, point clouds, building footprints, energy scores, transactions, zoning data and more. We validate it, transform it, index it and turn it into structured intelligence that powers our ICP engine, lookalike detection and quoting logic.

We are looking for a Geospatial Data Engineer who wants to own the backbone of that system.

This is not a visualization-only GIS role.

This is about large-scale geospatial pipelines, performance, indexing and correctness.

What you will do

You import, validate and transform large geospatial datasets into reliable internal schemas

You automate recurring imports from public datasets, partners and providers

You design and optimize spatial queries in Postgres + PostGIS

You build data pipelines that clean, normalize and reconcile messy spatial sources

You manage coordinate reference systems and projection transformations correctly

You work with formats such as Shapefile, GeoJSON, GML, LAS/LAZ and other geospatial standards

You handle point cloud internals and convert them into structured geometry

You design spatial indexing strategies using R-trees, H3 or similar structures

You optimize performance for large spatial joins, aggregations and proximity searches

You collaborate with AI and product engineers to expose geospatial intelligence through APIs

You help define internal data standards so that geometry, attributes and metadata remain consistent

Who you probably are

You have strong experience with Postgres and PostGIS in production

You understand spatial indexing and query performance deeply

You know what happens under the hood when doing a spatial join

You understand CRS systems, projections and transformation pitfalls

You are comfortable working with large datasets (millions of geometries)

You have worked with point clouds or at least understand their structure

You care about data correctness as much as performance

You are pragmatic and can balance purity with product needs

You enjoy building infrastructure that other engineers rely on

You communicate clearly with product, AI and backend engineers

Core knowledge

Postgres + PostGIS

Spatial query performance and optimization

General GIS concepts (CRS, projections, topology, geometry validity)

Geospatial formats (Shapefile, GeoJSON, GML, etc.)

Point cloud internals and file formats (LAS/LAZ)

Geospatial indexing structures (R-trees, H3, Quadtrees)

Nice to have

Rust experience (for performance-critical geometry or pipeline components)

Experience with GDAL/OGR

Experience with large national or cadastral datasets

Experience with cloud storage pipelines

Experience building geospatial APIs

Example projects you might own

Designing an automated pipeline to ingest national cadastral datasets and validate geometry consistency

Optimizing proximity queries to detect houses similar to a contractor’s best customers

Building a spatial indexing layer that allows real-time lookups across millions of buildings

Transforming raw point clouds into roof surface models usable by AI models

Creating recurring data refresh jobs with validation and alerting

Reducing spatial query latency from seconds to milliseconds

Why this role matters

Spotable’s AI is only as good as its geospatial foundation

If geometry is wrong, ICP matching breaks

If projections are wrong, measurements drift

If indexing is slow, the product feels unusable

If the data layer is solid, everything scales

Your work becomes the invisible engine that powers contractors across Europe and the US.

Apply

Send your CV or GitHub or LinkedIn to pj@spotable.com and tell us which geospatial systems you have built or scaled.

If it looks like a match, we move fast.

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