Data Engineering & AI - FinTech Investments / Unstructured Data
Summary
Builds and maintains data pipelines for ingesting, parsing, and structuring unstructured financial data (PDFs, HTML, XBRL) while integrating LLM-powered AI systems for retrieval and analytics in a FinTech context.
Requirements:
Data Ingestion
Build and maintain reliable pipelines for ingesting data from diverse external sources
Develop robust, schedulable, production-grade data ingestion workflows
Document Parsing & Structured Extraction
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
Data Warehouse Design
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
Pipeline Orchestration & Monitoring
Develop production ETL workflows using Airflow, Prefect, or Dagster
Implement data quality validation, monitoring, alerting, and data lineage
LLM Engineering
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
Backend Services
Build query APIs using FastAPI (or similar frameworks) to serve downstream analyst tools
Cloud Infrastructure
Deploy and operate systems on AWS or GCP
Leverage managed cloud services to minimize operational overhead
Requirements 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 Strongly Preferred 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 Nice to Have Experience building data infrastructure that supports NLP or LLM workloads Hands-on experience with dbt Experience designing multi-tenant systems and access control Familiarity with graph databases (e.g. Neo4j)
Application: Apply to this job posting, and send your CV with the job title as the subject line to: (HIDDEN TEXT) & https://www.linkedin.com/in/treasa-wong/
Requirements 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 Strongly Preferred 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 Nice to Have Experience building data infrastructure that supports NLP or LLM workloads Hands-on experience with dbt Experience designing multi-tenant systems and access control Familiarity with graph databases (e.g. Neo4j)
Application: Apply to this job posting, and send your CV with the job title as the subject line to: (HIDDEN TEXT) & https://www.linkedin.com/in/treasa-wong/