• Data Pipeline Architecture: Design, build, and maintain scalable data pipelines to ingest, clean, and process massive volumes of real estate data from fragmented sources, including MLS feeds, public tax records, and real-time user clickstreams.
• Data Mining & Advanced Analytics: Apply pattern recognition, association analysis, and clustering algorithms to extract actionable intelligence from property data, identify market trends, and segment customer behaviors.
• Data Imputation & Quality Assurance: Build robust imputation models to automatically detect anomalies and estimate missing/erroneous data (e.g., missing bedroom counts or inaccurate square footage) from raw MLS feeds, ensuring high accuracy for downstream valuation algorithms.
• Data Modeling & Storage: Design optimal Data Lake/Data Warehouse architectures to effectively store and manage a hybrid of structured data (prices, geospatial coordinates) and unstructured data (3D tours, property images, and legal documents).
• Real-time Data Strategy: Implement data streaming architectures (utilizing tools like Apache Kafka or AWS Kinesis) to support real-time data processing, ensuring low-latency updates for AI inference and property listings.
• Data Governance & Compliance: Establish strict data quality standards, encryption, and access controls to ensure the infrastructure strictly complies with data privacy laws, FinCEN reporting rules, and fair housing regulations.
• AI & ML Development: Develop and deploy core PropTech machine learning models, including deep learning-based property valuation algorithms (AVMs) and Contextual Bandits for personalized user recommendations.
• Multi-Agent AI Integration: Architect a multi-agent AI system that acts as a central coordinator, routing user intents (via function and tool calling) across different domain-specific skills such as property search, financing, and tour scheduling.
• Agentic Coding Execution: Utilize advanced AI coding assistants and agentic IDEs to automate data transformation scripts, generate infrastructure-as-code, and accelerate the prototyping of machine learning models.

Experience

• Minimum 3 years of proven experience combining Data Engineering and Machine Learning/AI development.
• Experience developing PropTech products is a strong advantage.

Knowledge and Skills

• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related technical discipline.
• Strong programming proficiency in Python and SQL for complex data manipulation and model development.
• Hands-on experience building data pipelines using cloud-based big data technologies (AWS, GCP, or Azure) and streaming platforms.
• Practical experience in training and deploying machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Proven ability to utilize Agentic Coding tools and workflows (AI-assisted IDEs, multi-agent parallel workspaces) to delegate tasks to AI and speed up software delivery.
• Ability to analyze document business requirements, and create data flows and diagrams.
• Skills in feature prioritization, and project management.
• Proficiency with tools like Jira, Trello, Notion.
• Excellent communication, persuasion, and teamwork skills.
• Ability to work under pressure, multitask, and adapt in a fast-paced start-up environment.

Preferred Qualifications

• Prior experience in the PropTech industry, specifically working with Multiple Listing Service (MLS) data, RETS/RESO Web APIs, or open-source AVM repositories.
• Familiarity with LLMs , RAG and building function-calling AI agents capable of executing real-world actions.
• Knowledge of high-cardinality geospatial data processing for location-based property analysis and valuation.
• Experience with multimodal AI and computer vision, particularly in analyzing property images, floor plans, and 3D home tours.
• Familiarity with real estate data compliance and security programs, including FTC Safeguards and algorithms that mitigate historical bias
• Familiarity with Agile/Scrum processes and MVP development.
• Fluency in English for working with international documentation or partners.

Management responsibility:

• Make daily reports/weekly/monthly.
• Perform periodic and irregular work reports at the request of the Product Manager and the Board of Directors.

Relationships:

• Internal: Closely associated with departments in the Company.
• External: Ability to build and develop good relationships.

Problems: 

• Directly deal with arising arising in the assigned work.
• Report and ask for timely direction of the Product Manager or The Board of Directors in case of arising is beyond the ability and authority to make personal decisions.

Accountability:

• Sense of responsibility and ability to withstand high pressure at work.
• Good moral character.
• Honesty, work in compliance with good discipline.
• Professional working attitude.
• Make efforts to learn more new knowledge.