Data Engineer

About this role

About Us:

Perpay is a certified B Corp and Philadelphia’s most impactful growth-stage startup. We are driven by a mission to significantly improve the financial stability of everyday Americans. For the past decade, we have established strong product-market fit and a profitable, efficient operating model across a suite of products, positioning Perpay as the premier financial partner for consumers with subprime credit.

With over 500,000 customers who have utilized more than $1 billion in spending power, we are at a pivotal moment. We are scaling our operations, building new offerings, and deepening our impact. We are looking for teammates eager to join us on this journey.

Our venture partners include First Round Capital and L Catterton.

Products we’ve built to make an impact:

• Perpay Marketplace: Combines interest-free payments and modern e-commerce to reduce cost of ownership and promote healthy repayment behavior.

• Perpay+: Leverages Marketplace repayment history to help members monitor and build credit with all 3 credit bureaus.

• Perpay Credit Card: Expands access to the flexibility and benefits of a World Mastercard by removing common barriers like high security deposits and low approval odds.

Our team thrives on in-person collaboration, operating from our unique center-city Philadelphia office. This comfortable "home away from home" space offers river views and fosters rapid product development, strong relationships, and career growth. The energy from achieving big wins is palpable here. While we primarily work in the office, we offer sensible flexibility for personal needs, such as sick children or urgent errands, and coordinate official remote weeks around major holidays. If you are passionate about a meaningful mission, collaboration, equity, and generous perks, Perpay is the best place to be in Philadelphia right now.

About the Role:

Our data team is organized across three groups: Data Engineering, Data Science, and Strategic Analytics. Data Engineering owns the warehouse, the orchestration layer, and the pipelines that move data from our operational systems to everyone who depends on it. This year, with the credit portfolio scaling and our modeling needs getting heavier, focus areas include real-time event ingestion, the risk decisioning service redesign, AI-agent data access on Redshift Serverless, ERP/EDI standardization with Finance and Accounting, and AI-assisted workflows across the engineering lifecycle. Data Engineers partner directly with Engineering, Risk, Commerce, Accounting, and Compliance, owning both the platform infrastructure and the business problems it's built to solve.

Our data engineering culture leans toward small teams owning meaningful surface area end-to-end. We write code that someone else has to maintain, we document the parts we wish someone had documented for us, and we'd rather argue about the right design in review than discover the wrong one in production. The stack: AWS throughout, Redshift and Spectrum for the warehouse, Glue and Fivetran for ingestion, Airflow for orchestration, ECS/ECR for services, DMS for replication, DataHub for cataloging and lineage, Terraform to hold it all together. Python and SQL everywhere. Spark when the size of the problem demands it. You don't need to have used all of this before, but you should have at least two years of production data engineering experience in a comparable environment.

What to Expect from the Role

What you should show up ready to teach anyone on your first day:

• How a healthy data engineering culture supports trustworthy production analytics, and what breaks first when that culture isn't there.

• Lessons you've learned from prior roles about data quality, pipeline reliability, or stakeholder communication that you'd want a new teammate to know.

• Design decisions on a data pipeline or platform you led, including the alternatives you considered and the trade-offs you actually made.

• Comfort moving across SQL, Python, orchestration, and infrastructure-as-code without needing one of them to be your specialty.

• Your favorite SQL pattern, modeling approach, or piece of data craft. We'll ask.

What you'll learn more about after you're hired:

• How Perpay's payroll-deduction business model shapes the data we collect, the cadence at which it lands, and the regulatory expectations around it.

• Our approach to building data products that work every time: scoping, modeling, code review, testing, observability, and lineage.

• Our one-year roadmap, long-term aspirations, and how the data team's priorities are tied to the rest of the business.

• Our stakeholders across