Senior Data Engineer (3464)

Ciklum

Що робити

The Data Engine (ETL/ELT): Design and implement the “Data Engine” responsible for extracting, cleaning, and materializing proprietary Real Estate data into secure, structured views for agent consumption
Multi-Layered Data Architecture: Provision and optimize a sophisticated data stack, including PostgreSQL (relational/OLTP), Redshift, Airflow,ChromaDB or PGVector (Vector Stores for embeddings), and GraphDB (Neo4j) for relational knowledge mapping
Agent-Data Interfacing: Develop and maintain Model Context Protocol (MCP) adapters and secure SDK endpoints, ensuring agents interact with data through governed interfaces rather than direct database access
Knowledge Graph Engineering: Build the initial Knowledge Graph using proprietary client data to model complex relationships between leases, entities, and CAM charges
RAG Infrastructure: Configure and maintain the Retrieval-Augmented Generation (RAG) knowledge base, including document ingestion pipelines and metadata management for lease abstracts and historical records

Data Engineering Mastery: Deep expertise in building production-grade ETL/ELT pipelines using Python and SQL; experience with Redshift, Airflow, dbt is highly valued
AI Data Stores: Hands-on experience with Vector Databases (Chroma, PGVector) and Graph Databases (Neo4j or Cognee) for AI-native applications
Cloud Infrastructure: Proven track record of managing data services within the AWS stack, including RDS, S3, and event-driven architectures
Security & Compliance: Experience building data solutions that meet SOC 2 and GDPR standards, with a focus on data masking and zero-trust access patterns
Domain Expertise: Familiarity with Commercial Real Estate data structures (Leases, CAM, Invoices) is a significant advantage

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