Data Engineer - Finance

Weekday AI•San Francisco, CA

9d•Onsite

About The Position

This role is for one of our clients. A rapidly growing AI organization is seeking a Data Engineer — Finance to build and maintain the data infrastructure that powers its finance operations. You'll join a lean, high-impact team responsible for developing scalable data pipelines, improving data quality, and supporting critical financial initiatives. This is a 6-month, full-time onsite contract with the possibility of extension based on performance and business needs.

Requirements

2–4 years of professional experience as a Data Engineer.
Strong hands-on experience with PySpark and distributed data processing frameworks.
Advanced SQL skills with experience writing efficient, production-grade queries.
Experience building, maintaining, and optimizing large-scale ETL/data pipelines.
Ability to work independently while collaborating effectively across cross-functional teams.
Bachelor's degree in Computer Science or a related technical discipline.
Excellent written and verbal communication skills.
Willingness to work onsite full-time.

Nice To Haves

Experience supporting finance, accounting, or enterprise business data platforms.
Familiarity with cloud-based data infrastructure and modern data engineering best practices.
Strong analytical and problem-solving abilities with attention to data accuracy.
Experience working in fast-paced, high-growth technology environments.

Responsibilities

Design, build, and maintain robust PySpark-based data pipelines to ensure accurate and timely financial data processing.
Write, optimize, and troubleshoot complex SQL queries to extract, transform, and validate large-scale financial datasets.
Collaborate closely with finance and engineering stakeholders to translate business requirements into reliable data infrastructure solutions.
Own end-to-end data quality by identifying, investigating, and resolving pipeline issues efficiently.
Improve the reliability, scalability, and performance of data workflows supporting finance operations.
Contribute to architecture discussions and recommend best practices for data engineering and pipeline optimization.
Document workflows, data models, and engineering processes to ensure maintainability and knowledge sharing.