Lead development and operations of geospatial data pipelines and lakehouse systems. Migrate orchestration to Prefect, extend sources and transforms, optimize Postgres and Iceberg stores, build observability and AI-agent readiness, and integrate cross-team tooling with product and ML teams.
About Us
The role
What you’ll do
You might be a good fit if you…
Our Stack
Working at Aarden
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 GitHub, 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")
You might be a good fit if you…
Must-have
- 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
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
Our Stack
Languages: Python and SQL. TypeScript/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, GeoPandas, 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.
Working at Aarden
Aarden is a high-trust, high-output team. We’re 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 developer tooling and support. You’ll work hard here, and we’ve got your back.
Practically, this means you’ll be asked to take on large projects, have a high bar of expectations to meet, and have a strong support system to help you meet that high bar. That support system includes:
- At least 2 in-person days per week at our office in Capitol Hill | We’ve found that while heads-down time at home is fantastic for task-related productivity, in-person time is magic for longer-form productivity. Our in-person days are used to plan, troubleshoot, and check-in with each other on progress and questions. Expect team lunches and whiteboarding.
- Focused ownership in your role | The rest of the team is here to help you and cares deeply about the long-term functionality of our applications. With that said, we’ll be looking to you to own your lane, go deep, and develop a strong stance on what it takes to make our applications best-in-class.
- Dedicated monthly AI tooling budget | We’re in a golden era of AI-powered developer tooling. We strongly encourage augmenting your output with AI tools, and have a dedicated & flexible budget for every team member to support that setup. We care about what you ship, not how.
The base pay range for this role is $150,000 – $180,000 per year.
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