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 (along with some data science oriented projects as well).
The ideal candidate (and where the team falls short currently):
You must have experience building AI infrastructure (end-to-end) such as setting up a RAG, vector database, and an agent framework/orchestrator (while being strong with Python/SQL). With this in mind, you should know how to use LLMs, building out solutions that can integrate with the firm’s data environment.
i.e. You have experience delivering an AI solution that leverages a RAG. End to end project execution is highly preferred. You should be able to display this in your project-based work.
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 field
- 3-7 years of experience; additional experience may be considered in lieu of degree
- Strong 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 warehouses
- Proficiency in data preprocessing, cleaning, and transformation techniques for both structured and unstructured data sources
- Advanced SQL expertise, ideally with Snowflake, including optimization/security best practices
Nice To Haves (Not Required):
- ML frameworks (TensorFlow, PyTorch)
- Experience with supervised and unsupervised learning algorithms, model evaluation metrics, and ML deployment in production environments
- Experience with large language models (LLMs), prompt engineering, and NLP frameworks (e.g., Hugging Face Transformers) for document processing and information extraction
- Develop and implement OCR/NLP models to extract, validate, and classify key information from loan agreements, property reports, and other financial documents
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