Senior Data Engineer

About Courted

Courted is the leading AI-powered platform for real estate recruiting, retention, and market intelligence. We help brokerages and teams understand agent performance, recruit strategically, and retain top talent—all in one place.

The real estate industry moves over $100B in annual commissions, and it's still powered by spreadsheets, cold calls, and guesswork. We're building at the intersection of machine learning and a massive industry that's just beginning to modernize. We're an early-stage, high-growth SaaS company shipping fast, working smart, and building transformative tools for the next generation of real estate leaders.

We're looking for a Senior Data Engineer who loves solving problems and thrives in a fast-paced environment. You'll work end-to-end across the ETL process—managing integration across a variety of toolsets and helping our team grow to introduce new datasets into our system.

At Courted, our data is our foundation—our commitment to consistency, coherence and completeness is what keeps users coming back every day. Through strong tools, processes and patterns, we've adapted a huge number of real estate data sources to serve customers across the country. On this team, you'll be responsible for plugging into our environment to identify key efficiencies and guide us as we scale to an ever-increasing user base.

Applicants should have a zeal for analytical thinking, practical problem solving and quality. This role is for someone who has lived in large datasets and is excited to join a small, but high-octane team that can push the boundaries of what the industry has been able to offer brokerages and agents to date.

• Investigate and ingest new datasets to expand analysis with new insights + design novel ways of correlating disparate datasets
• Collaborate with our product and design teams to turn insights into functional features in our application
• Engage with our engineering team to align on data contracts, dependencies, and coordination between the ETL and the application
• Manage rigorous QA process for internal validation before releases
• Engage with Customer Success to ensure the ongoing validity and value of datasets to end users

• At least 5 years of full-time experience in data engineering (data analysis, development of ETL code, and opinionated experience with ETL infrastructure)
• Strong expertise in Python, PySpark, SQL and Spark optimization—this is the foundation of our stack
• Deep experience with Databricks. Someone who has really cut their teeth and knows the ins-and-outs of a modern Databricks deployment will feel right at home.

• Strong experience working in agile teams, ideally within a SaaS or fast-paced startup environment
• Familiarity with modern development tools: AWS, Postgres, Django, Circle
  
  CI (or similar), Docker, Insomnia/Postman (or similar)
• Curious and proactive mindset—you ask questions, seek context, and quickly learn new tools or technologies to get the job done
• Strong end-to-end engineering process—from identifying the right problems to solve, to exploring solutions, and making smart tradeoffs along the way
• Close attention to detail—you care about polish and consistency, ensuring your work feels complete and ready for launch
• Systems thinker who can simplify complex, often messy data into clean interfaces for consistent insights
• Organized executor—you manage your time well, communicate clearly, and follow through to deliver thoughtful, high-quality work
• Interest in AI and data-driven products, with a desire to understand how things work under the hood

• Real ownership: You'll manage data pipelines end-to-end—from APIs (or even FTP servers 😣) to production—and see your work go live to thousands of users daily
• Growth: Join as one of the early technical hires with room to shape the team and stack as we scale
• High velocity: Tight feedback loops, minimal bureaucracy, maximum learning
• Visibility: Work directly with data providers and end-users who rely on your work to power their daily process

We've built the most comprehensive real estate dataset in the country—covering more than 95% of all…