Senior Data Engineer — AI-Native Data Platform

Aarden•Seattle, WA

7d•$150,000 - $180,000•Hybrid

About The Position

Aarden is hiring a high-autonomy senior data engineer to own and evolve its core datasets and the pipelines that keep them current. The datasets combine property, geospatial, and AI-derived intelligence to power Aarden’s insights and products. The role is responsible for data quality and the systems behind it, with opportunities to contribute directly to user-facing products. The engineer will own the day-to-day operation and reliability of Aarden’s data infrastructure, improve pipeline performance, observability, and data quality, and extend pipelines for new sources and transformations. They will collaborate with product and analytics teammates, maintain useful documentation, and evaluate relevant data and geospatial technologies through focused experiments and recommendations. Aarden is a high-trust, high-output team. The role involves substantial ownership and challenging projects, supported by teammates and dedicated AI tooling resources. The position is Seattle-based, with at least two in-person office days per week in Capitol Hill; candidates must be able to commute to the office.

Requirements

At least 12 months of continuous ownership of the operation of one or more production data pipelines.
Experience with relational databases and lakehouse architectures.
Strong Python skills.
Experience with workflow orchestration such as Prefect, Airflow, Dagster, or similar.
Curiosity and excitement about AI and its impact on developers’ ways of working.
Comfort working in a git-based, CI-friendly workflow.
At least 5 years of professional experience.

Nice To Haves

Experience with geospatial data and associated tooling such as GDAL, GeoParquet, PostGIS, Apache Sedona, or similar.
Experience in full-stack environments where work can directly affect the application layer.
Comfort with PySpark or similar distributed data processing.
Experience with Apache Sedona or other cloud spatial-compute platforms.
Experience building observability or logging layers for data pipelines, rather than only application services.
Experience with socioeconomic, property, parcel, real estate, or land data.
Experience using AI agents to improve data architecture in a production codebase.
Experience in real estate or land, energy, forestry, or agriculture technology.
Experience in data scraping, ideally via agents.

Responsibilities

Own the day-to-day reliability and operation of the data infrastructure, including S3, Iceberg, Postgres, Lambda, PMTiles, and raster datasets.
Monitor, troubleshoot, and improve pipeline performance, observability, and data quality.
Extend existing pipelines to support new data sources and transformations.
Use automation and AI-assisted workflows where they can make operations more reliable or efficient.
Work with product to build datasets that support new features.
Co-develop intelligence products with the analytics team.
Keep pipeline and dataset documentation useful and current.
Stay current on relevant data and geospatial technologies, identify opportunities to improve the stack, run focused experiments, and recommend changes when the benefits justify the tradeoffs.
Own the development and evolution of the data products and platform in the role’s area of responsibility.