Data Acquisition Engineer

Parcell•Remote,

10h•Remote

About The Position

Behind every state we launch is a mountain of data—parcels, school ratings, transit, flood zones, crime indicators, and dozens of state-specific layers—that has to be found, understood, and brought into our platform before scoring can happen. Much of it doesn't come with a clean download button. It lives in undocumented municipal portals, inconsistent county feeds, and sources where the only "API" is whatever an embedded map happens to call. We are looking for someone who finds that kind of source irresistible—part data detective, part engineer, part problem solver—who can track down hard-to-find data, get meaningful information out of it, and turn it into something our platform can rely on. You'll be the technical right hand to our implementation team: they know what each state's scoring needs, and you figure out how to get it and make it real. This role is ideal for someone who likes end-to-end ownership, technical problem solving, and visible impact.

Requirements

A track record of getting data out of difficult, poorly documented sources—you've reverse-engineered an undocumented endpoint, scraped a stubborn portal, or wrangled a malformed feed into something clean, and you find that kind of puzzle satisfying rather than frustrating.
Solid working proficiency with PostGIS—reprojecting between coordinate systems, validating and repairing geometry, spatial indexing, and spatial joins. You don't need to be a GIS specialist, but you're well past "I've used ST_Intersects."
Strong SQL skills (PostgreSQL) with the ability to write queries for analysis, validation, and troubleshooting.
Hands-on experience shipping and maintaining production code in a typed language (we use Node.js/TypeScript; strong experience in Go, Java, or Python transfers well).
A reproducibility instinct—you treat "I can re-run this cleanly next quarter" as part of the job, not an afterthought.
Excellent attention to detail and comfort working with messy data and edge cases.
Strong communicator able to collaborate with our implementation team, software engineers, and external data providers.

Nice To Haves

Passionate about affordable housing and real estate development is a plus.

Responsibilities

Track down the external data our scoring needs—including poorly documented, inconsistent, or hard-to-access sources—and confirm you can reliably get usable data out of them.
Reverse-engineer undocumented portals, APIs, and feeds to extract meaningful data.
Evaluate new and existing sources for coverage, quality, and fit against each state's requirements and scoring criteria.
Figure out how messy, real-world data has to be reshaped to be useful, and get it into our platform in a form scoring can trust.
Work with varied formats (shapefiles, GeoJSON, CSVs, county/state feeds) and reproject spatial data correctly across coordinate systems.
Load, structure, and index geospatial and tabular data in PostgreSQL/PostGIS using our internal tooling and scripts (Node.js, Bash, SQL).
Make every data pull repeatable—a re-runnable path to refresh each source on its cadence, not a one-time extraction only you know how to repeat.
Monitor upstream sources for format or schema changes and update pulls before they break a launch.
Validate spatial and tabular data beyond row counts—geometry validity, topology, and coverage gaps—to ensure accuracy before it reaches scoring.
Document where each dataset lives, how it was sourced, and how to refresh it.