Full Stack Analytics Engineer

ABOUT THE ROLE

Interval Partners is a multi-billion-dollar alternative investment firm located in midtown Manhattan. We are seeking a Full Stack Analytics Engineer to join our team. This is a hands‑on, ownership‑oriented role: you will build and maintain alternative data pipelines, develop forward‑looking KPI estimates, and design analytics and tools that directly support portfolio managers and analysts. The ideal candidate is intellectually rigorous, comfortable working end‑to‑end across data, modeling, and tooling, and motivated by turning complex datasets into clear, actionable investment insight.

KEY RESPONSIBILITIES

Data Pipelines & KPI Estimation

• Collaborate with the data team to build and maintain robust ingestion pipelines for alternative datasets. 
• Transform raw vendor data into clean, analysis-ready outputs that map to fundamental KPIs such as revenue, same store sales, GMV, and others.

Analyst and Portfolio Manager Support

• Dig into the "why" behind trends you and the analysts see in the data.
• Translate analyst hypotheses into testable frameworks and deliver clear, well-documented findings with appropriate statistical context and caveats.
• Proactively surface insights and data-driven alerts that may be relevant to existing or prospective positions. 
• Implement requests from Analysts and PM’s for their reporting needs.

Dashboards & Visual Analytics

• Design and maintain custom web applications that allow analysts to explore, compare, combine, and benchmark signals across multiple alternative data providers for a given company or sector. Own the full lifecycle from backend data layer through frontend UI. Collaborate with vendor to ensure product meets business needs.
• Build intuitive, interactive visualizations of trends, seasonal adjustments, revision histories, and cross-provider discrepancies within the firm's custom analytics platform. 
• Work with the investment team to iterate on dashboard design and ensure outputs align with how PMs and analysts make decisions. 
• Architect and maintain the firm's alternative data web application, including backend APIs, data models, and frontend components, iterating on design with the investment team to ensure outputs map to how PMs and analysts make decisions. 

AI Agent Integration

• Structure and expose processed alternative data outputs in formats consumable by internal AI/LLM-based research agents (e.g., retrieval-augmented generation pipelines, structured APIs). 
• Collaborate with the data and technology teams to ensure data is contextualized, well-documented, and retrievable in ways that maximize its utility for automated research workflows. 
• Build and maintain MCP servers and AI Agents to allow analysts to query, explore, and synthesize alternative data conversationally. 
• Design and implement LLM-powered research workflows on cloud infrastructure (Azure), including retrieval-augmented generation pipelines, structured tool-use agents, and conversational interfaces over alternative data. 
• Drive the transition of alternative data consumption from static, report-based delivery toward interactive, on-demand access through LLM agents, custom dashboards, and web applications  

Multi-Source Forecasting Models

• Develop quantitative models that combine signals from multiple alternative data providers to generate forward-looking KPI estimates (e.g., quarterly revenue nowcasts, consensus-relative surprise probabilities). 
• Rigorously evaluate model performance, document assumptions, and communicate confidence intervals and known limitations to the investment team. 
• Continuously improve models by incorporating new data sources, refining aggregation methodologies, and learning from forecast errors.

Data Scouting, Backtesting & Onboarding

• Proactively identify and evaluate new alternative data vendors and datasets through independent research, industry conferences, and broker relationships. 
• Conduct thorough back tests of candidate datasets: assess coverage, historical depth, survivorship bias, look-ahead bias, and signal-to-noise ratio versus public benchmarks. 
• Manage vendor relationships, trial negotiations, and technical onboarding for approved datasets; maintain a living catalogue of the fund’s alt data assets.

QUALIFICATIONS

Required

• 4–6 years of experience in a data analysis, data science, quantitative research, or data engineering role.
• Proficiency in Python (pandas, numpy, scikit-learn, statsmodels), comfortable working with large datasets. 
• Familiarity with cloud platforms (Azure preferred) for deploying data pipelines, APIs, and LLM-based workflows. 
• Experience with containerized deployments (Docker) and CI/CD practices. 
• Demonstrated experience building and owning end-to-end data pipelines in a production environment. 
• Ability to map alternative data observations to fundamental drivers. 
• Solid grasp of statistical methods, time-series analysis, and the pitfalls of backtesting (overfitting, multiple testing, survivorship bias). 
• Excellent written and verbal communication skills; ability to present complex quantitative work clearly to non-technical stakeholders. 
• Experience building web applications (e.g., React, Next.js, FastAPI, Flask, or similar frameworks) with both backend and frontend components. 
• Highly organized, self-directed, and comfortable managing multiple concurrent projects in a fast-paced investment environment.

Preferred

• Direct prior experience working with alternative data vendors. 
• Familiarity with equity research, the earnings estimate process, and sell-side consensus data providers (FactSet, IBES, Bloomberg). 
• Experience building LLM-integrated data products or retrieval-augmented generation (RAG) pipelines. 
• Advanced degree (MS or PhD) in Statistics, Computer Science, Applied Mathematics, Engineering, or related quantitative discipline.

This is a fully onsite 5 days a week role based in our Midtown office. Benefits: Full medical & vision, 401k. 

 Compensation range is 120k-150k