We are looking for a data engineer / geospatial developer who can help us build the first version of a facility intelligence dataset. The goal is to extract industrial facilities from OpenStreetMap and enrich them with company ownership and facility type information using APIs such as Google Places, business databases, and other enrichment methods. Scope of Work The project will include the following steps: 1. Extract industrial facilities from OpenStreetMap Use Overpass API or OSM datasets to pull locations tagged as: building=warehouse building=industrial industrial=manufacturing industrial=logistics landuse=industrial Target output: US-wide or pilot markets such as: Texas California Midwest freight hubs Deliverable: Clean dataset containing: latitude longitude OSM tags facility type 2. Clean and Normalize Data Transform raw JSON into a structured dataset. Each row should include: OSM ID coordinates building type industrial tag land use tag Remove non-relevant locations such as: scrap yards empty industrial land storage lots utility infrastructure 3. Enrich Facilities with Business Data Use APIs such as: Google Places API Google Maps other business lookup APIs to identify: company name business category address facility type Categorize facilities as: manufacturing plant distribution center logistics hub warehouse unknown 5. Deliver Final Dataset Final dataset should include: | company | facility_type | industry | lat | lon | city | state | Target scale: Pilot dataset (10–20 cities) Eventually scalable to entire United States Skills Required Ideal candidate has experience with: Python APIs geospatial datasets OpenStreetMap / Overpass API Google Places API JSON data processing data pipelines Nice to have: logistics industry experience supply chain analytics geospatial analysis web scraping Deliverables Script to pull facility data from OpenStreetMap Script to enrich data using Google Places API Clean dataset of facilities Documentation explaining the pipeline