Data Engineer, Real Estate Intelligence
Role summary
FlipOps is seeking a Data Engineer to build and maintain the data infrastructure for its real estate intelligence platform. This role involves designing and managing ETL pipelines for diverse property datasets, optimizing data models for scoring and valuation, integrating third-party APIs, and ensuring data quality. The ideal candidate will have strong programming skills in Python and SQL, experience with data warehousing and cloud platforms, and a solid understanding of data modeling and ETL processes. Familiarity with real estate data and machine learning support is a plus. This is a remote, full-time position.
Company Description
FlipOps is an advanced, all-in-one platform designed to simplify real estate investment processes by replacing multiple tools with a single system. Specializing in distressed property evaluation, FlipOps identifies high-potential opportunities, scores seller motivation, and streamlines deals from discovery to closure. Leveraging cutting-edge technology, including machine learning models, the platform helps real estate wholesalers, flippers, and buy-and-hold investors predict deal outcomes efficiently. Our goal is to empower investors to focus on closing more deals and analyzing less. FlipOps is tailored for professionals seeking innovative and reliable automation in real estate intelligence.
Role Description
The Data Engineer will build and maintain the data infrastructure that powers FlipOps' core intelligence features, including distress scoring, lead prioritization, property valuation models, and skip tracing integrations. This role requires experience working with large-scale property datasets and an understanding of how real estate investors use data to identify opportunities, analyze deals, and make acquisition decisions.
Qualifications
- Experience in Data Engineering and building scalable data infrastructure
- Proficiency in Data Modeling and creating well-structured data for analytics
- Strong knowledge of Extract, Transform, Load (ETL) pipelines and processes
- Expertise in Data Warehousing and methods for efficient data storage and retrieval
- Familiarity with Data Analytics tools and practices to generate insights
- Solid programming skills in languages such as Python, SQL, or similar
- Ability to work independently and contribute in a remote environment with cross-functional teams
- Experience with cloud platforms and real estate-related data is a plus
- Bachelor’s degree in Computer Science, Data Science, or a related field, or equivalent professional experience
What You'll Do
- Design and maintain ETL pipelines that ingest property records, tax data, lien filings, pre-foreclosure notices, probate records, and MLS data from multiple sources
- Build and optimize the data models that power FlipOps' distress scoring algorithm, ensuring accuracy across different property types, markets, and distress indicators
- Integrate third-party skip tracing APIs and develop quality scoring for returned contact data, measuring hit rates by list type and data provider
- Develop the data architecture for comp analysis tools, normalizing property attributes like square footage, lot size, condition, and renovation scope across inconsistent data sources
- Build pipelines that surface motivated seller signals in near-real-time: new liens filed, missed tax payments, code violations, ownership changes, and pre-foreclosure activity
- Create and maintain the datasets used by machine learning models for lead scoring, deal outcome prediction, and ARV estimation
- Monitor data quality and freshness across all property data sources, flagging when a provider's accuracy drops or coverage gaps appear in specific markets
- Collaborate with the product team to expose data insights within the investor-facing platform, including pipeline analytics, lead source performance, and conversion metrics
You Might Be a Fit If
- You've built ETL pipelines that process property or real estate data at scale and understand the inconsistencies that come with county-level records, MLS feeds, and third-party aggregators
- You're proficient in Python, SQL, and at least one modern data orchestration tool like Airflow, Dagster, or Prefect
- You've worked with property data APIs or providers like ATTOM, CoreLogic, PropStream, or BatchLeads and know where the data is reliable and where it requires significant cleaning
- You have experience building or supporting machine learning models in production, particularly around scoring, classification, or prediction tasks
- You've designed data warehouses or lakehouses using tools like Snowflake, BigQuery, Redshift, or Databricks and can make architecture decisions based on query patterns and data volume
- You understand what ARV, MAO, equity position, and distress indicators mean in the context of real estate investing, or you're prepared to learn quickly
- You've dealt with data deduplication challenges — matching property records across sources where addresses are formatted differently, owner names are inconsistent, and parcel numbers don't always align
- You care about data freshness and have built monitoring systems that alert when pipeline latency or source accuracy degrades
Bonus Points
- Direct experience in the real estate or proptech space, working with investor-facing data products
- Familiarity with skip tracing data pipelines and the challenge of maintaining high contact rates as phone numbers and addresses go stale
- Experience with geospatial data processing — driving for dollars route optimization, property clustering by neighborhood, or market heat mapping
- Background in building recommendation or ranking systems, particularly for surfacing high-priority items from large candidate pools
- You've worked with DNC/TCPA compliance datasets and understand how to integrate regulatory data into outreach tooling
Compensation
$78,000-$85,000. Remote-friendly.