Software Engineer Ii, Data & AI

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

Software Engineer II, Data & AI at Ripple, focusing on data ingestion and transformation for analytics and ML. The role involves building AI agents and conversational tools for data insights, with exposure to LLM-based tools and data pipelines. Requires proficiency in Python/Scala, SQL, data warehousing (e.g., Databricks), distributed systems, and AWS. Experience with CI/CD and Terraform is also needed.

What you'd actually do

  1. Highly efficient in shipping solutions to both large and small projects.
  2. Can handle ambiguity in requirements and can define and propose solutions for them.
  3. Writes, presents, and gets agreement on the design document for a project highlighting the architecture, timelines and alternatives considered.
  4. Owns the development and rollout for a small to mid-sized projects.
  5. Writes clean tech specs and identifies risks before starting major projects.

Skills

Required

  • Python
  • Scala
  • SQL
  • Databricks
  • distributed systems
  • RESTful APIs
  • server-side APIs integration
  • AWS cloud resources
  • GitLab
  • Terraform

Nice to have

  • AI or machine learning in data-related projects
  • coding assistants
  • chat-based data tools
  • search tools that answer questions from documents
  • basic workflow automation with AI

What the JD emphasized

  • core engineers within Ripple’s central Data Engineering
  • data ingestion and transformation for analytics, machine learning
  • AI agents and conversational tools that let business users get insights from data
  • helping with data pipelines, data quality checks, or simple AI-powered tools
  • tools that use large language models, such as coding assistants, chat-based data tools, search tools that answer questions from documents, or basic workflow automation with AI

Other signals

  • data ingestion and transformation for analytics, machine learning
  • AI agents and conversational tools that let business users get insights from data
  • helping with data pipelines, data quality checks, or simple AI-powered tools
  • tools that use large language models, such as coding assistants, chat-based data tools, search tools that answer questions from documents, or basic workflow automation with AI