In this role, you will serve as a senior technical leader for the geospatial data and tooling capabilities that support a large-scale project. You will set technical direction for how geospatial data is sourced, evaluated, prepared, and delivered through AI/ML pipelines, while solving the organization’s most complex GIS engineering challenges.

You will work across imagery and other raster data, vector data, point clouds, and terrain and land-cover datasets. You will define scalable approaches for acquiring data from open, public-domain, and commercial sources, and you will help shape strategic partnerships with data providers. You will establish standards for coverage, freshness, quality, provenance, licensing, and cost so that geospatial datasets can be used reliably at scale.

You will architect and evolve automated systems that clean, validate, resize, reformat, align, and postprocess geospatial data for internal AI/ML and product teams. Using open-source technologies such as GDAL and PROJ, along with internally developed tools, you will guide the development of durable, observable, and reusable workflows rather than one-off processing solutions.

This is a highly cross-functional individual contributor role. You will partner with AI/ML, Pipeline, Product, and leadership teams to translate product vision into a long-term geospatial data strategy. You will also provide technical mentorship, raise engineering standards, lead design reviews, and influence decisions across teams without relying on direct authority.

Responsibilities

• Own the technical strategy and architecture for sourcing, preparing, validating geospatial datasets across target regions.
• Lead the resolution of ambiguous, high-impact GIS engineering problems that span multiple data modalities, systems, and teams.
• Define standards and measurable controls for data coverage, freshness, quality, provenance, licensing, reliability, and cost.
• Evaluate open, public-domain, and commercial sensor data sources, and provide technical leadership for provider selection and data partnerships.
• Architect scalable geospatial processing systems using open-source and internally developed tooling to align, crop, resample, transform, validate, and otherwise prepare data for use.
• Design and guide automated postprocessing workflows for the outputs of geospatial AI/ML pipelines, with attention to performance, repeatability, observability, and maintainability.
• Make meaningful hands-on contributions to internal software and tools used to source, process, catalog, and distribute geospatial datasets.
• Partner with AI/ML teams to anticipate model-development data needs and ensure access to diverse, representative, and fit-for-purpose datasets.
• Partner with Pipeline and Product teams to define data requirements, technical interfaces, quality thresholds, and launch-readiness criteria for new regions and capabilities.
• Lead technical design reviews and drive alignment on architecture, tradeoffs, sequencing, and risk across partner teams.
• Mentor engineers, share domain expertise, and establish engineering practices that improve the quality and velocity of the broader GIS function.
• Communicate complex technical decisions, dependencies, and risks clearly to engineering leaders, product partners, and other stakeholders.
• Partner closely with leadership to connect the long-term product vision to an actionable geospatial data and technology roadmap.