Sr. Data Platform Engineer

Aarden AI•Seattle, WA

10d•Hybrid

About The Position

Aarden is a land intelligence platform that helps landowners, investors, and developers figure out what a piece of land can actually be used for, and how to market it. We turn messy parcel, infrastructure, market, community, and ecological data into clear, bankable answers for land-dependent assets. Our goal is to become the default decision layer for land: helping physical projects start in places where they can be built and supported for decades. We’ve built out a suite of data products to support that goal — pipelines, databases, and AI/ML models that power our maps and In-app agents. We have strong product-market fit, and we're now focused on augmenting our data systems. We're looking for a Product-Focused data engineer to maintain and evolve our geospatial data pipeline. Our core data asset is a unique blend of property data, geospatial data, and AI-native derived data. Alongside advocating for data excellence, you’ll be empowered to opportunistically contribute to our user-facing product.

Requirements

Have strong Python skills & are comfortable with PySpark or similar distributed data processing
Have a strong sense of how the data you’re working with impacts the end-user
Are curious and excited about AI and the impact it can have on our ways of working as developers
Have experience with geospatial data (GeoParquet, PostGIS, Apache Sedona, or similar)
Have worked with table formats like Apache Iceberg and lakehouse architectures
Have worked on workflow orchestration (Prefect, Airflow, Dagster, or similar)
Are comfortable working in a git-based, CI-friendly workflow

Nice To Haves

Have worked in full-stack environments, where your work can directly impact the application layer
Have experience with Apache Sedona or other cloud spatial-compute platforms
Have built observability/logging layers for data pipelines (not just app services)
Have experience with property, parcel, real estate, or land data specifically
Have experience using AI agents to improve data architecture in a real production codebase
Experience in real estate/land, energy, forestry, or agriculture tech

Responsibilities

Continue our migration of pipeline orchestration to Prefect
Own day-to-day operations of our data infrastructure
Extend the pipeline to include new data sources and transformations
Maintain, expand and optimize our postgres database and Iceberg datalake
Create the 'connective tissue' for data at Aarden
Partner with product on new feature-driven datasets.
Collaborate with the ML/analytics team to close the loop: anomaly detection → ticket → fix → validation → promotion to production
Develop cross-team tooling/infra to keep GitHub, Notion, Linear, and Slack connected so pipeline issues, docs, and fixes stay linked
Implement run-over-run data observability (row counts, key column distributions) to catch anomalies and bugs
Expose accuracy/quality metrics as first-class artifacts so changes can be evaluated automatically, by a human or an agent
Write and maintain AI-context documentation (schema docs, pipeline architecture, known patterns/quirks, "what not to do")