Data Engineering & AI - FinTech Investments / Unstructured Data

Build and maintain reliable pipelines for ingesting data from diverse external sources

Extract structured data from PDF, HTML, XBRL, and other document formats
Handle scanned PDFs, complex tables, multilingual documents (English & Chinese), and other real-world document challenges

Design and maintain well-structured schemas on modern cloud data warehouses such as Snowflake, BigQuery, or PostgreSQL
Build scalable, maintainable data models for downstream analytics
Develop production ETL workflows using Airflow, Prefect, or Dagster
Implement data quality validation, monitoring, alerting, and data lineage
Build production applications using OpenAI, Anthropic, and open-source LLMs
Develop structured output pipelines and Retrieval-Augmented Generation (RAG) workflows
Design lightweight evaluation frameworks and quality monitoring for LLM-powered systems
Build query APIs using FastAPI (or similar frameworks) to serve downstream analyst tools
Deploy and operate systems on AWS or GCP
Leverage managed cloud services to minimize operational overhead

5–10 years of experience building and operating production software systems
Strong proficiency in Python and SQL
Solid experience with data modeling, testing, and performance optimization
Hands-on experience parsing messy real-world documents (PDF, HTML, XBRL, etc.)
2–3 years of production experience building and deploying LLM-powered applications
Experience with at least one workflow orchestration framework (Airflow, Prefect, or Dagster)
Experience designing schemas for at least one modern cloud data warehouse
Excellent communication skills, with the ability to collaborate closely with research teams and company leadership
Professional working proficiency in English

Previous experience as a Founding Engineer or early engineering hire at an AI startup
Built end-to-end data platforms in an early-stage startup environment

See also