Data Engineer - Finance
About The Position
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. 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.
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.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
