Data Engineer

Link Logistics•New York, NY

2d•$140,000 - $155,000•Hybrid

About The Position

Link Logistics Real Estate (“Link”) is hiring a hybrid ML/Backend Engineer to own the intelligence layer of Link's Analytics Engine. This role involves designing pipelines to activate disparate real estate data, building knowledge graphs and retrieval systems for LLM querying, and architecting backend services to deliver real-time insights to investment teams. The engineer will collaborate with investment analysts and asset managers to identify analytical gaps and build solutions combining classical ML, graph-based reasoning, and LLM-native workflows.

Requirements

4+ years in ML engineering, backend engineering, or a role spanning both
Hands-on experience shipping LLM-powered applications in production — RAG pipelines, prompt engineering, eval frameworks
Strong Python skills; comfortable owning backend services and APIs end-to-end
Experience with knowledge graphs or graph databases (Neo4j or similar)
Proficiency building data pipelines at scale (Spark, Databricks, or equivalent)
Deep sensitivity to data provenance — a track record of building systems that create analyst trust, not just claim it

Responsibilities

Design graph-based data structures encoding relationships across markets, assets, tenants, and transactions.
Build retrieval pipelines (RAG, hybrid search, structured queries) that give LLMs accurate, contextually rich grounding.
Develop rule-based and agentic LLM workflows that automate investment analytical tasks, including prompt engineering, eval frameworks, and production reliability.
Build ETL/ELT workflows that ingest, normalize, and enrich large-scale internal and third-party datasets (property records, leasing data, macro signals, alt data), ensuring clear ownership, update cadences, and quality SLAs for every dataset.
Build systems that make data trustworthy by design, including lineage tracking, confidence scoring on derived outputs, and clear attribution for data provenance.
Develop and maintain APIs and services that expose ML outputs and structured data to front-end applications, prioritizing low-latency, reliability, and clean contracts.
Work directly with investment and asset management teams to understand analytical needs and iterate quickly, treating analyst trust as a first-class product requirement.