Staff Software Engineer, Data Engineering

Ripple Ripple · Fintech · San Francisco, CA +1 · Engineering

Staff Software Engineer, Data Engineering role at Ripple, focusing on designing and building the Caspian Data Platform. The role involves leading technical direction for data ingestion, transformation, governance, and quality, with a significant emphasis on applying AI/ML to data engineering workflows, including agentic systems and automated transformation generation. Requires deep expertise in Databricks, SQL, Python, and AWS, with a strong track record in data platform architecture and operations.

What you'd actually do

  1. Lead the design of the core pillars of the platform — ingestion, transformation, governance, and data quality — and build the most critical components yourself.
  2. 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.
  3. 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.
  4. Lead the most complex, ambiguous initiatives that span multiple quarters and teams, from problem statement to delivered capability.
  5. 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.
  6. Act as the quality standard bearer: raise code quality, build rigor, and engineering judgment across the team, and mentor senior engineers into greater ownership.

Skills

Required

  • 10+ years of data engineering experience
  • architecting and operating data platforms at scale
  • Databricks (Delta Live Tables, Unity Catalog, Delta Lake, Spark)
  • SQL
  • Python
  • AWS

Nice to have

  • Experience driving AI tooling into data engineering workflows
  • building agentic systems
  • integrating LLMs into developer workflows
  • enabling conversational analytics
  • influence through technical credibility
  • translate tradeoffs for both engineers and leadership

What the JD emphasized

  • deeply hands-on
  • Deep mastery of Databricks
  • building agentic systems
  • integrating LLMs into developer workflows
  • enabling conversational analytics

Other signals

  • AI for data engineering
  • agentic systems for pipeline operations
  • automated transformation generation
  • self-serve analytics interfaces