Data Engineer – Finance

NVIDIA•Santa Clara, CA

22h

About The Position

NVIDIA has been a leader in computer graphics, PC gaming, and accelerated computing for over 25 years, driven by innovation and talented individuals. The company is now leveraging AI to define the next era of computing, with GPUs acting as the brains for various intelligent systems. As an NVIDIAN, you will work in a diverse and supportive environment. NVIDIA is seeking an experienced Data Engineer for its Finance AI and Data Science team to continuously create and improve high-performance data pipelines. These pipelines will support both traditional analytics and modern AI initiatives. The role involves collaborating with AI developers and finance professionals to develop robust, scalable data products and effectively communicate technical solutions 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.