GIS Data Science Engineer, Data Ingestion and QA/QC

Terra AI•Redwood City, CA

About The Position

About Terra AI We are building the state-of-the-art AI platform for the discovery and development of clean energy and mineral resources. We bring the most advanced techniques in generative AI, foundation modeling, and autonomous decision optimization to tackle the most important problems in the geosciences. These systems can help more reliably identify critical resource deposits, more rapidly measure and characterize them, and design more efficient and sustainable production plans. We are backed by Khosla Ventures and other leading venture investors. We are now looking to grow our team from ~15 to ~30 by the end of the year to continue to mature our technology and support deployment with our world-class mineral and clean energy partners. Role Own customer data ingestion and build QA/QC workflows that make messy, real-world geoscience data usable at scale. This role blends hands-on GIS execution with data engineering. You will run GIS workflows for projects while also building automation to reduce manual work and improve repeatability. What you’ll do Run project GIS workflows Prepare, curate, and serve complex client datasets across surface GIS and 3D subsurface contexts. Use GIS tools day to day to assemble layers, validate spatial alignment, and produce project outputs. Create high-quality maps and figures for internal review and client deliverables, including cartographic polish when needed. Build ingestion and QA/QC systems Ingest and normalize datasets such as: Drillhole and drill core data (major focus) Airborne and other geophysical survey datasets Supporting geospatial layers and project metadata Standardize disparate client formats into a unified framework to support modeling workflows. Build automated QA/QC checks that catch issues early, including: Coordinate reference systems and transformations Units, conventions, missingness, duplicates, and outlier detection Schema validation, metadata sanity, provenance tracking Cross-dataset consistency checks (for example, collars vs surveys vs intervals) Create reproducible ingestion pipelines that reduce manual work and shorten time-to-first-model. Act as a primary liaison for geology and ML teams, providing spatial analysis and visual validation of outputs against client-provided datasets. Document standards and build tooling that is usable by other engineers and scientists.

Requirements

Formal training and hands-on experience in GIS, geospatial data science, geoscience data systems, or a closely related discipline.
Strong practical ability with messy data, including building pipelines, validations, and repeatable transformations.
Proficiency with modern GIS software.
Experience with QGIS and/or ArcGIS Pro is strongly preferred.
Experience with spatial data administration: coordinate system transformations, managing large datasets, and working with spatial databases.
Ability to build in Python for data workflows.

Nice To Haves

Experience producing publication-quality technical figures (Adobe Creative Suite or similar).
Familiarity with subsurface or geo-modeling tools (Leapfrog, SKUA GOCAD, Mira Geoscience Analyst, or similar).
Experience with drillhole data standards and common exploration formats.
Experience with geophysical datasets and processing concepts.
Experience designing QA/QC systems where wrong data has real cost.

Responsibilities

Run project GIS workflows
Prepare, curate, and serve complex client datasets across surface GIS and 3D subsurface contexts.
Use GIS tools day to day to assemble layers, validate spatial alignment, and produce project outputs.
Create high-quality maps and figures for internal review and client deliverables, including cartographic polish when needed.
Ingest and normalize datasets such as: Drillhole and drill core data (major focus) Airborne and other geophysical survey datasets Supporting geospatial layers and project metadata
Standardize disparate client formats into a unified framework to support modeling workflows.
Build automated QA/QC checks that catch issues early, including: Coordinate reference systems and transformations Units, conventions, missingness, duplicates, and outlier detection Schema validation, metadata sanity, provenance tracking Cross-dataset consistency checks (for example, collars vs surveys vs intervals)
Create reproducible ingestion pipelines that reduce manual work and shorten time-to-first-model.
Act as a primary liaison for geology and ML teams, providing spatial analysis and visual validation of outputs against client-provided datasets.
Document standards and build tooling that is usable by other engineers and scientists.