Data Engineer – Finance

NVIDIA•Us, CA

About The Position

NVIDIA is hiring a Data Engineer for the Finance AI and Data Science team. We seek an experienced Data Engineer to continuously create and improve high-performance pipelines. These pipelines support both our traditional analytics and modern AI. You will collaborate with AI developers and finance professionals to develop robust, scalable data products and communicate technical solutions clearly across the organization.

Requirements

Bachelor's or Master’s degree in a quantitative field (e.g. Statistics, Computer Science, Business Analytics, Data Science, Economics, or a related area) or equivalent experience.
5+ years of experience, including at least 4 years specifically in data engineering.
ETL/ELT experience in modern Data Platforms such as Snowflake, Databricks, or AWS/Azure/GCP equivalents is required.
Experience with Git, building and maintaining CI/CD pipelines, and using an orchestrator (e.g., Airflow) is also needed.
Familiarity with 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.
Background with non-tabular data formats (JSON, XML, PDF, Excel, PowerPoint, etc.), APIs and other non-traditional data sources.
Experience supporting a business focused data science team (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

Combine business insight and the data engineering toolkit to support critical business process automation, BI, data science, and agentic AI initiatives.
Trace complex 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 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.