Top Real Estate Data & Analytics Jobs in Los Angeles, CA

Design and implement large-scale data warehouses, optimize data pipelines using SQL and dbt, and mentor teams in data best practices.

The Analytics Engineer develops data models and KPIs, ensuring data reliability for decision-making while collaborating across multiple departments at Crexi.

In this role, you'll build and scale high-throughput data pipelines, ensure data quality, and leverage AI for improved data operations. You'll also lead initiatives in a fast-paced environment focused on real estate data engineering.

As a Senior Data Engineer, you will design and maintain scalable data models, build production data pipelines, and support analytics across various business functions.

Lead design and governance of enterprise data models across Databricks Lakehouse, Snowflake, and MongoDB. Partner with business and engineering teams to translate requirements into high-performance dimensional and relational models, establish medallion architecture and modeling standards, optimize performance, and enable analytics, ML/GenAI, and reporting use cases.

The Pricing Analyst will develop pricing solutions, manage data inputs, perform financial analysis, and collaborate across teams to support global transaction management pursuits.

Design and deploy ML and NLP models (including LLM evaluation) to predict borrower risk and property valuations, advise business teams, mentor junior analysts, and foster data-driven experimentation.

The Senior Analyst will conduct economic analysis, prepare housing plans, and lead reports and presentations for clients while engaging in community development efforts.

The Systems Analyst, Debt will analyze business needs and create scalable technology solutions for managing commercial real estate debt investments, including building data pipelines and automating workflows.

The Program Director of Data & Analytics at Prologis will lead teams in transforming data through AI, overseeing budgets, resource allocation, and ensuring effective use of analytics tools while driving adoption and continuous improvement across data initiatives.