San Francisco, United States
Data Engineer - Finance
Weekday AI · San Francisco · United States
Posted: 7/29/2026 · Last checked: 10/12/2026
Job description
Quoted in full from Weekday AI's job posting. Gavroche does not edit it.
This role is for one of our clients
Compensation: $60 - $85 per hour
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.
Location: Onsite – San Francisco, CA / New York, NY / Bellevue, WA
Requirements
Key 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.
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.
Preferred Qualifications
- 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.
Role Details
- Employment Type: Independent Contractor
- Duration: 6-month contract with potential extension
- Work Arrangement: Full-time, Onsite
- Locations: San Francisco, CA / New York, NY / Bellevue, WA
Note: This is a hands-on data engineering position focused on building and maintaining production data infrastructure. It is not a business intelligence, reporting, dashboarding, machine learning, or AI research role.
Equal Opportunity
We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
Contract & Payment Terms
- You will be engaged as an independent contractor.
- This is a full-time onsite engagement.
- Projects may be extended, shortened, or concluded early based on business needs and performance.
- Your work will not involve access to confidential or proprietary information from any employer, client, or institution.
- Payments are made weekly via Stripe or Wise based on services rendered.
- Please note: We are unable to support H1-B or STEM OPT candidates at this time.
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200 open roles like this one; at most three per employer shown.
What data engineer adverts in the United States state
Middle half: 142,864 – 196,250 USD a year; median 170,000 USD.
From 105 adverts by 62 employers that state a salary, annualised.
How data engineer roles in the United States are worked
14% fully remote · 0% hybrid · 86% do not say
Of 43 adverts. An advert that does not state a work mode is counted as not saying, not as on-site.
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