Senior Data Engineer

PropertyPilot•Manasquan, NJ

About The Position

PropertyPilot is an early-stage, AI-powered real estate platform that aggregates 170+ million tax and parcel records nationwide—ownership data, property assessments, comparable sales, mortgage information, and foreclosure filings—into a single, mobile-first experience. Real estate is the largest asset class in the U.S. at over $85 trillion, with residential real estate alone representing roughly $50 trillion in household wealth and more than $2 trillion in annual transactions. The brokerage business that sits on top of that activity is equally massive: more than 1.5 million licensed agents, over 100,000 brokerage firms, and roughly $100 billion in commissions paid every year. Yet it remains one of the most fragmented, data-starved, and least efficient industries in the country. The property data that powers every deal is scattered across thousands of county assessor offices, MLS systems, and legacy enterprise providers with clunky interfaces, stale records, and six-figure price tags. PropertyPilot is building the modern, mobile-first property intelligence layer for the operators those incumbents overlook—the agents, teams, investors, lenders, insurers, and builders who actually close deals. We combine public record data, market signals, and AI-driven workflows into one experience professionals can pull up from their phone between showings. That’s our niche: professional-grade property data and AI, priced and designed for the long tail of the real estate industry rather than only the top 1%. Backed by an experienced SaaS founder and a seasoned product, engineering, and design team, we’re building the tools that bring transparency and speed to real estate professionals, banks, insurers, and builders. We are operating before product-market fit—growth at PropertyPilot is about getting the right users in the door, learning from real usage, and iterating quickly. The Role PropertyPilot is, at its core, a data company—the product is only as valuable as the data underneath it. Every screen our users see, every AI feature we ship, every insight we surface, and every customer we win sits on top of the pipelines this role owns. Get the data layer right and the rest of the company compounds on it; get it wrong and nothing else we build matters. Put simply, this job is the floor the rest of PropertyPilot stands on. We are looking for a Senior Data Engineer to design, build, and maintain the data infrastructure that powers PropertyPilot’s property intelligence platform. You’ll own the pipelines that transform millions of records across several datasets—170+ million tax and parcel records, ownership histories, comparable sales, mortgage and lien data, foreclosure filings, geospatial layers, and a growing set of API and web sources—into the reliable, high-quality datasets our product, analytics, and engineering teams depend on every day. As our data footprint grows into the hundreds of millions of records and beyond, you will be the person making sure it stays fast, clean, and trustworthy. This is a hands-on environment by design. Leaders at PropertyPilot execute. You won’t be managing a team at the start—you’ll be directly responsible for architecting ingestion pipelines, optimizing our AWS data stack, and ensuring data quality across the platform. If you need a tidy org chart and narrowly defined swim lanes to be effective, this isn’t the role. If you thrive when you’re the person holding the data layer together, keep reading.

Requirements

5-7 years of experience designing, building, and operating ETL/ELT pipelines at scale.
Expertise with AWS data tools (e.g., S3, Glue, Athena, Redshift, EMR) and cloud-native architecture patterns.
Proficiency in SQL and data modeling, including dimensional modeling and performance tuning.
Experience handling large and complex datasets—ingesting, transforming, and serving them reliably in production.
Strong problem-solving and debugging skills across distributed data systems.

Nice To Haves

Experience with GIS / geospatial data processing.
Knowledge of spatial databases or geospatial formats (PostGIS, GeoJSON, Shapefile, Parquet with geospatial extensions).
Experience with data orchestration tools (e.g., Airflow, Dagster, Prefect).

Responsibilities

Design and maintain scalable data ingestion pipelines, including the compliant extraction of data from APIs and publicly available web sources.
Design, build, and maintain ETL/ELT pipelines that power analytics, product features, and internal tooling.
Manage large-scale data ingestion and transformation workflows across structured and semi-structured sources.
Work with cloud-native data solutions on AWS—S3, Glue, Athena, Redshift, and EMR—to build performant, cost-efficient data platforms.
Ensure data quality, reliability, and performance through monitoring, validation, and observability practices.
Collaborate with analytics, product, and engineering teams to deliver the datasets they need to ship features and make decisions.
Optimize data workflows for scalability and cost, continuously tuning infrastructure as data volumes grow.