Sr. Data Platform Engineer

About Us

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. That’s where you come in.

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.

Pipeline modernization

• 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

Cross-team integration

• 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 Git Hub, Notion, Linear, and Slack connected so pipeline issues, docs, and fixes stay linked

Observability & AI-agent readiness

• 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")

Must-have

• Have strong Python skills & are comfortable with PySpark or similar distributed data processing
• Have a strong sense of how the data you  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 (Geo Parquet, 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

Strongly preferred

• 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

Nice To have

• Have experience using AI agents to improve data architecture in a real production codebase
• Experience in real estate/land, energy, forestry, or agriculture tech

Languages: Python and SQL. Type Script/Node is a plus for our Application layer and AWS ingest paths.

Orchestration & compute

• Prefect 3 for pipeline orchestration (YAML/config-driven flows, retries, logging)
• Coiled for elastic EC2 workers on GDAL-heavy and batch Python jobs
• Wherobots (managed Apache Sedona / PySpark) for large-scale spatial joins, parcel ingest, and lakehouse work

Data lake & formats: Apache Iceberg, Cloud-Optimized GeoTIFF (COG), and PMTiles. Queried with PySpark and DuckDB.

Geospatial: GDAL, rasterio, Geo Pandas, and tippecanoe. Large-scale spatial work runs on Sedona/Spark via Wherobots.

Databases & serving: PostgreSQL + PostGIS (and pgvector on the app side) as the production store.

Aarden is a high-trust, high-output team. We  striving to be intentional about our team growth. This allows us to test the outer boundaries of our individual capabilities, while also going deeper on…