Data Engineer

About Allocate

Allocate is transforming private market investing by enabling RIAs and family offices to seamlessly discover, model, and manage their private market exposure.

Our platform combines curated fund and co-investment opportunities with institutional-grade infrastructure. Through a single, data-rich digital experience, clients access top-tier opportunities across venture capital, private equity, private credit, and other private asset classes—backed by powerful tracking, analytics, and administration tools.

Job Description

Allocate is looking for a Senior Data Engineer to help build out the data infrastructure that powers our analytics, reporting, and data-driven product features. As a fintech startup on a mission to make investing in top-tier private markets more accessible, we have a wealth of financial and investment data to harness. Our data lead has established the foundational architecture and strategy, and we are now looking for a strong senior engineer to help extend, scale, and harden it. In this role you will partner closely with our data lead to model core financial entities, integrate internal and external sources, and build the pipelines and infrastructure that let our engineering and product teams make informed decisions and ship compelling features. This is a hybrid position based out of our Palo Alto, CA office, where you will work alongside our backend team (C#/.NET) and frontend team (Node/Vue.js) to integrate data pipelines into our platform. If you are a hands-on engineer who wants to do high-impact data work in a collaborative startup environment, we want to hear from you.

Responsibilities

• Build and Extend Data Architecture: Build on and extend Allocate's data lakehouse on AWS, combining data lake storage and warehouse technologies to store diverse financial datasets. Contribute to our knowledge graph that models key relationships (investors, funds, companies, etc.) and to the vector database integration that stores embeddings for semantic search and retrieval across our AI agents, models, and providers.

• Develop Data Pipelines: Create robust ETL/ELT pipelines to ingest, clean, and transform data from various sources (internal application data and third-party APIs). Ensure both batch processing and real-time data streaming are handled to support up-to-date analytics and recommendations. Build pipelines with an eye on scalability (able to handle increasing data volume and complexity) and reliability (proper error handling and monitoring).

• Enable AI/ML Capabilities: Work closely with our data science and engineering team to provision the data and infrastructure needed for machine learning models and AI features. This includes preparing training datasets, setting up feature stores, and orchestrating workflows that feed LLM-based agents with the context they need (e.g. retrieving relevant data via vector similarity search). You will also help implement systems to serve AI model outputs (such as recommendations) back into the product in real time.