Senior Data Engineer
at Creditgenie
Plymouth Meeting, PA; Philadelphia, PA; New York, NY, hybrid, senior, full time, USD 1,80,000–2,10,000 / year, 5+ years
posted 04 Sept 2026
- Airflow
- Git
- Snowflake
- dbt
- CI/CD
- ETL
- ELT
- AWS
- SQL
- Python
About the role
Posted: 2026-09-04
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 https://www.linkedin.com/in/ed-harycki/, former Swift Capital Founder (acquired by PayPal in 2017 https://techcrunch.com/2017/08/10/paypal-acquires-swift-financial/). 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.
Join our Data Engineering 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 would do
- Design, development, and operate reliable data pipelines for first-party and third-party data across batch, streaming, and change data capture (CDC) patterns.
- Lead the technical design of ETL/ELT and orchestration workflows, selecting appropriate patterns and tradeoffs for performance, scalability, maintainability, and cost.
- Develop high-quality SQL and Python solutions for transformation, validation, automation, and complex data processing, while setting a strong standard for maintainable engineering practices.
- Design and strengthen data quality, freshness monitoring, alerting, and observability so issues are detected and resolved before they materially impact downstream users.
- Optimize Snowflake workloads, storage, and pipeline architecture with attention to reliability, scalability, platform performance, and cost efficiency.
- Lead investigation of complex production data incidents, identify root causes across systems, and implement durable fixes that improve platform resilience and reduce recurrence.
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