Data Engineer - Đi Làm Ngay

· 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.