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.
This is a broad / generalist focused role with a heavy focus on Data Engineering first and foremost (although there may be some data science oriented projects as well).
Responsibilities :
- Build automated reporting systems and interactive dashboards for portfolio monitoring, including custom analyses for executive leadership, asset management, and origination
- Implement machine learning (AI) models for asset valuation, market analysis, and investment opportunity screening
- Build and optimize Snowflake databases and queries to support real-time business intelligence needs
- Design and implement quality assurance processes for data extraction, transformation, and analysis workflows
- Design and maintain scalable data pipelines in Nexla and Python to integrate property management systems, financial databases, and market data feeds into Snowflake DW
- Create predictive models to identify asset performance trends, risks, and opportunities across the real estate portfolio, with a focus on occupancy rates and NOI metrics
- Design 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 field3-7 years of experience; additional experience may be considered in lieu of degreeStrong Python programming with proficiency in Python requests libraries (pandas, numpy, scikit-learn)Experience building and optimizing ETL pipelines using modern data platforms (they use Nexla) and working with Snowflake or similar cloud data warehousesProficiency in data preprocessing, cleaning, and transformation techniques for both structured and unstructured data sourcesAdvanced SQL expertise, ideally with Snowflake, including optimization / security best practicesNice To Haves (Not Required) :
ML frameworks (TensorFlow, PyTorch)Experience with supervised and unsupervised learning algorithms, model evaluation metrics, and ML deployment in production environmentsExperience with large language models (LLMs), prompt engineering, and NLP frameworks (e.g., Hugging Face Transformers) for document processing and information extractionDevelop and implement OCR / NLP models to extract, validate, and classify key information from loan agreements, property reports, and other financial documentsID : 44163