Data Engineer, Document AI – Finance

NVIDIA•Us, CA

About The Position

NVIDIA is hiring a Data Engineer within the Finance AI and Data Science team. We are looking for an experienced Data Engineer to constantly innovate and turn complex challenges into high-performance pipelines that power our traditional analytics and modern agentic AI. Your role will involve ingesting and processing unstructured documents into our data platform, working closely with AI developers and finance experts to develop strong, scalable data products that convert raw text into actionable intelligence.

Requirements

Bachelor's or Master’s in a quantitative field such as Statistics, Computer Science, Business Analytics, Data Science, Economics, or equivalent experience.
5+ years of experience, with at least 4 years in data engineering.
ETL/ELT experience in modern Data Platforms such as Snowflake, Databricks, or AWS/Azure/GCP equivalents is required.
Experience with Git and building and maintaining CI/CD pipelines is also needed.
Familiarity with orchestrators like Airflow and testing tools like pytest or Great Expectations is important.
Ability to write readable and maintainable code (primarily in SQL, Python/PySpark), knowledge of scientific libraries for data processing (Numpy, SciPy, Pandas).
Experience collaborating with IT, InfoSec, business partners, and data scientists to build end-to-end data pipelines that ensure data accuracy and quality across relational databases, data lakes, and warehouses.
A passion for data engineering backed by a basic understanding of statistics and machine learning, with the communication skills necessary to translate technical status to diverse collaborators.

Nice To Haves

Experience with SAP and/or Salesforce.
Practical experience building and deploying Graph-RAG and similar systems, using a wide variety of data formats (e.g., JSON, XML, PDF, Word, Excel, PowerPoint, etc.).
Experience assisting a data science group centered on business functions (finance, sales ops, HR, marketing, supply chain, etc.).
Experience with app development frameworks like Flask and/or Streamlit, data versioning tools like DVC, and data processing tools like dbc.
Familiarity with regulated data, and building pipelines with compliance requirements.

Responsibilities

Build and optimize pipelines that extract insights from complex financial documents like SEC filings, contracts, and tax reports.
Use innovative tools and techniques to help humans and AI agents search and perform multi-step reasoning across different document types.
Combine business insight and the data engineering toolkit to support critical business process automation, BI, data science, and AI initiatives.
Trace data workflows to source systems, then develop and deploy accurate and optimized data pipelines, using modern scheduling, automation, and data orchestration tools.
Develop deep knowledge of financial data and requirements, working directly with collaborators and owning projects end-to-end on a diverse set of finance and finance-adjacent data sets.
Integrate AI into data workflows, applying sensible and secure agentic models as a core component of our data framework.
Deliver an audit-ready source of truth by implementing strict data quality and lineage standards, ensuring all technical solutions translate into clear, actionable insights for collaborators.