About this role

▸about Ripple — company background

At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.

If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.

Role Overview

At Ripple, we're building a world where value moves like information does today. It's big, it's bold, and we're already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.

If you're ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.

As a Staff Software Engineer on the Data Engineering team, you set the technical direction for the Caspian Data Platform — Ripple's centralized lakehouse powering analytics, financial reporting, product intelligence, and data-driven operations across every business unit. You own the architecture for ingestion, transformation, governance, and data quality across the platform, and you stay deep in the code while you, do it. You are the bar raiser for the team: the engineer who sets the standard for how data is built at Ripple, and the others learn from by working alongside you.

What you'll do

Lead the design of the core pillars of the platform — ingestion, transformation, governance, and data quality — and build the most critical components yourself.

Define the engineering patterns and reference architectures for Databricks pipelines, and stay hands-on shipping production flows, writing the reference implementations, and prototyping the hard parts before the team scales them.

Establish the benchmark for data quality and governance — expectations, data contracts, lineage, validation frameworks — and hold the team to it through building and code review.

Lead the most complex, ambiguous initiatives that span multiple quarters and teams, from problem statement to delivered capability.

Lead the technical direction for applying AI across data engineering. This includes agentic systems for pipeline operations, automated transformation generation, and self-serve analytics interfaces. Develop the first versions yourself.

Act as the quality standard bearer: raise code quality, build rigor, and engineering judgment across the team, and mentor senior engineers into greater ownership.

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