Data Engineer - Finance
- Salary
- $60 - $85 per hour
- Level
- Mid
Not stated in the posting: work type, visa.
Open. First seen 2 October 2026.
About the role
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.
What this role asks for
Particular to this role
Machine Learning
Rare in Weekday AI's other 299 open postings.
Standard for data engineering roles
ETL · SQL · Spark
Common in data engineering postings at any company.
Read against Weekday AI's other open postings on this site.
The team: AI Training HIRES STEADILY
This role is one of 108 open in AI Training. 1 was posted in the last 30 days and the oldest in April: a team that hires all year rather than one being built.
WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS
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