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:
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How a healthy data engineering culture supports trustworthy production analytics, and what breaks first when that culture isn’t there.
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Lessons you’ve learned from prior roles about data quality, pipeline reliability, or stakeholder communication that you’d want a new teammate to know.
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Design decisions on a data pipeline or platform you led, including the alternatives you considered and the trade-offs you actually made.
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Comfort moving across SQL, Python, orchestration, and infrastructure-as-code without needing one of them to be your specialty.
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Your favorite SQL pattern, modeling approach, or piece of data craft. We’ll ask.
What you’ll learn more about after you’re hired:
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How Perpay’s payroll-deduction business model shapes the data we collect, the cadence at which it lands, and the regulatory expectations around it.
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Our approach to building data products that work every time: scoping, modeling, code review, testing, observability, and lineage.
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Our one-year roadmap, long-term aspirations, and how the data team’s priorities are tied to the rest of the business.
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Our stakeholders across Risk, Commerce, Marketing, Ops, and Finance: who they are, what they need from data, and how to partner with them on solving the right problems.