Geospatial/Data Engineer for LandScope

Job title Geospatial Data Engineer — Python, SQL, GCP, BigQuery, DuckDB Spatial Job description We’re building LandScope, a land-intelligence platform that combines parcel, planning, electricity-grid and environmental data to help infrastructure and development teams identify and assess land opportunities. Ireland is our initial market, but the role is fully remote and prior experience with Irish datasets is not required. We already have a substantial working data foundation and codebase. We are looking for a strong, hands-on geospatial/data engineer to help us implement and improve backend spatial-data workflows. This is primarily a data engineering + geospatial engineering role, not a frontend role and not a basic GIS mapping role. The type of work you may be doing Building and improving Python/SQL geospatial data pipelines Processing large parcel and polygon datasets Spatial joins, intersections, distances and geometry calculations Working with BigQuery, GCS and Cloud Run Working with DuckDB Spatial, GeoParquet/Parquet and potentially PostGIS Rebuilding spatial relationships against versioned parcel geometries Improving source ingestion and repeatability Helping move workflows away from local-machine dependencies Improving data provenance, versioning and reproducibility Producing clean structured outputs that can later be consumed by APIs and the product UI Our datasets can contain millions of spatial features, so experience beyond small shapefile projects is particularly valuable. Technology we currently use Python SQL Google Cloud Platform BigQuery / BigQuery GIS Google Cloud Storage Cloud Run DuckDB / DuckDB Spatial GeoParquet / Parquet GeoPandas / Shapely PostgreSQL / PostGIS where appropriate You do not need experience with every item above. You should apply if You have strong practical experience with at least 3–4 of the following: Python geospatial processing SQL / spatial SQL Large geospatial datasets BigQuery GIS DuckDB Spatial GeoParquet / Parquet PostGIS GeoPandas / Shapely / GDAL Cloud data pipelines Spatial joins and intersection workflows Data pipelines with reproducible/versioned outputs Experience with cadastral or parcel data, utilities, infrastructure, planning, property, energy or public-sector geospatial data is useful but not required. Please do not apply if your experience is mainly QGIS or ArcGIS desktop mapping Map visualisation only Frontend/web development Power BI or dashboard development AI/LLM applications without strong data-engineering experience Basic CRUD application development Very small GIS datasets only We need someone comfortable working with the underlying spatial data and pipelines, not just displaying maps. How the role will work The founder will continue to own: product direction overall architecture domain rules what LandScope should and should not build We want someone who can take a clearly defined technical problem, investigate it properly, implement it and explain any issues or better approaches they identify. The initial workload will likely be around 5–10 hours per week, with the opportunity to increase if the fit is good. We are bootstrapped, so we are looking for someone who provides excellent technical ability and value for money, rather than an agency or large consultancy. Budget $20–$35 USD/hour, depending on experience. We may go slightly higher for an unusually strong candidate with directly relevant geospatial/data-platform experience. Application questions Please answer all of the following. Generic applications that do not answer the questions will not be considered. 1. What is the largest geospatial dataset you have personally worked with? Please give an approximate number of features/rows and explain what processing you performed. 2. Which of these have you actually used: BigQuery GIS, DuckDB Spatial, GeoParquet or PostGIS? Tell us briefly what you built with them rather than simply listing technologies. 3. Describe one spatial join, intersection or geometry-processing pipeline you personally implemented. What were the inputs, approximate scale and main technical challenge? 4. Imagine you have approximately 3 million parcel polygons and need to calculate relationships against another large polygon or infrastructure dataset. How would you approach it without simply loading everything into memory? 5. Please include a GitHub profile, code sample or relevant technical portfolio if available. If you cannot share previous code because of confidentiality, that is fine—just explain. Please begin your application with the words: PARCEL DATA so we know you have read the full description. Initial hiring process For the strongest candidates, we may offer a small paid technical trial before starting a longer engagement. The trial would involve a representative geospatial-data task such as reading GeoParquet data, performing a spatial relationship calculation and producing a clean structured output. We will pay for the trial.