About the Role
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 others learn from by working alongside you.
Responsibilities
- 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.
Requirements
- 10+ years of data engineering experience, with a strong track record of architecting and operating data platforms at scale — while remaining deeply hands-on.
- Deep mastery of Databricks — Delta Live Tables, Unity Catalog, Delta Lake, and Spark — with the judgment to make platform-level decisions and the skill to implement them.
- Demonstrated ownership of ingestion, transformation, governance, and data quality across a production platform.
- Expert in SQL and Python, applied to complex data modeling, transformation, and platform tooling.
- Strong AWS experience operating data systems at scale.
- A history of being the technical bar raiser — setting standards and patterns that other engineers build on, and lifting the quality of everyone around you.
- The ability to influence through technical credibility and translate tradeoffs for both engineers and leadership.
Nice to Have
- Experience driving AI tooling into data engineering workflows — building agentic systems, integrating LLMs into developer workflows, or enabling conversational analytics.
