Data Engineer
US$180,000 – US$210,000 / yr base salary
Posted 4 Sep 2026
Advertised as “Senior Data Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$180,000 – US$210,000 / yr |
|---|
Field of studyComputer Science, Engineering (or related)
Study levelBachelor's
Company
Credit Genie is a mobile-first financial wellness platform designed to help individuals take control of their financial future. We leverage artificial intelligence to provide personalized insights and are building a financial ecosystem by offering tools and services that provide instant access to cash, and building credit. Our goal is to empower every customer to achieve long-term financial stability.
Founded in 2019 by Ed Harycki, former Swift Capital Founder (acquired by PayPal in 2017). Backed by Khosla Ventures and led by industry pioneers from companies such as; PayPal, Square, and Cash App, we are well positioned to build the future of inclusive finance through cutting-edge technology and customer-centric solutions.
Overview
Join our Data Platform & Analytics team and help shape the trusted data foundation that powers analytics, strategy, and downstream applications. As a Senior Data Engineer, you will independently own the design and operation of scalable data pipelines and platform capabilities across our Snowflake and AWS environment. You will solve complex, cross-functional data problems, exercise strong technical judgment, and partner across the organization to make data reliable, accessible, and ready for a growing set of business and technical use cases.
What You'll Do
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Design, development, and operate reliable data pipelines for first-party and third-party data across batch, streaming, and change data capture (CDC) patterns.
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Lead the technical design of ETL/ELT and orchestration workflows, selecting appropriate patterns and tradeoffs for performance, scalability, maintainability, and cost.
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Develop high-quality SQL and Python solutions for transformation, validation, automation, and complex data processing, while setting a strong standard for maintainable engineering practices.
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Design and strengthen data quality, freshness monitoring, alerting, and observability so issues are detected and resolved before they materially impact downstream users.
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Optimize Snowflake workloads, storage, and pipeline architecture with attention to reliability, scalability, platform performance, and cost efficiency.
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Lead investigation of complex production data incidents, identify root causes across systems, and implement durable fixes that improve platform resilience and reduce recurrence.