Data Engineer

Our client, a real estate and asset-based lender, is looking to hire a full-time Analytics Engineer to work onsite out of their Midtown Manhattan location.

This is a dynamic team focusing on optimizing the firm's asset management operations and business intelligence (BI) capabilities.

This role combines technical data engineering expertise with analytical science skills to drive data-informed decision-making across their portfolio.

Responsibilities:Build automated reporting systems and interactive dashboards for portfolio monitoring, including custom analyses for executive leadership, asset management, and originationImplement machine learning (AI) models for asset valuation, market analysis, and investment opportunity screeningBuild and optimize Snowflake databases and queries to support real-time business intelligence needsDesign and implement quality assurance processes for data extraction, transformation, and analysis workflowsDesign and maintain scalable data pipelines in Nexla and Python to integrate property management systems, financial databases, and market data feeds into our Snowflake data warehouseDevelop and implement OCR/NLP models to extract, validate, and classify key information from loan agreements, property reports, and other financial documentsCreate predictive models to identify asset performance trends, risks, and opportunities across the real estate portfolio, with a focus on occupancy rates and NOI metricsDesign and optimize ETL processes to ensure data quality/consistency, with robust monitoring and alert systems Qualifications:Bachelor's or Master's Degree in Computer Science, Data Science, or related field with 3-7 years of experience; additional experience may be considered in lieu of degreeExpert-level Python programming with strong proficiency in data science libraries (pandas, numpy, scikit-learn) and ML frameworks (TensorFlow, PyTorch)Experience building and optimizing ETL pipelines using modern data platforms (they use Nexla) and working with Snowflake or similar cloud data warehousesDemonstrated experience with large language models (LLMs), prompt engineering, and NLP frameworks (e.g., Hugging Face Transformers) for document processing and information extractionProficiency in data preprocessing, cleaning, and transformation techniques for both structured and unstructured data sourcesExperience with supervised and unsupervised learning algorithms, model evaluation metrics, and ML deployment in production environmentsAdvanced SQL expertise, particularly with Snowflake, including optimization and security best practices ID: 44163