Senior Software Engineer, Data Engineering

You will be a core builder of the Caspian Data Platform, Ripple's centralized lakehouse that powers analytics, financial reporting, product intelligence, and data-driven operations across every business unit. You will own the design and delivery of production-grade pipelines end to end, raise the engineering bar through code reviews, technical design, and mentorship, and operate with high autonomy on complex, cross-functional data initiatives, bringing sound judgment to tradeoffs around performance, cost, reliability, and maintainability.

Responsibilities

• Design, build, and operate production ETL pipelines on Databricks using Delta Live Tables and Spark, applying medallion architecture patterns across Finance, Payments, Product, and GTM domains
• Own data modeling in different layers, defining governed, reusable table structures that serve as reliable sources of truth for business metrics and downstream consumption
• Drive technical design for projects spanning multiple teams, writing clear design documents that articulate architecture, tradeoffs, and rollout plans
• Build pipeline observability into your work including failure detection, alerting, retry logic, and operational hygiene that keeps oncall burden low
• Integrate AI tooling into data workflows including agentic systems for pipeline triage, automated code generation for transformations, and natural language interfaces for data access
• Mentor engineers, reviewing their code and designs and helping them grow into more complex ownership
• Contribute to platform infrastructure through Terraform, and work with Unity Catalog to establish data contracts, access controls, and lineage

Requirements

• 5–8 years of hands-on data engineering experience building and maintaining production pipelines and data models
• Deep proficiency with Databricks or similar platforms — Delta Live Tables, Unity Catalog, Delta Lake, and PySpark or Spark SQL as primary tools
• Strong SQL skills: complex transformations, incremental materialization, schema evolution, and data quality validation
• Solid AWS experience relevant to building and operating data systems at scale
• Proficiency in Python for data engineering — ingestion, transformation, testing, and utility tooling
• Experience with CI/CD tooling such as GitLab and infrastructure-as-code with Terraform
• Exposure to AI tooling in a data context — whether building agents, using LLMs for code generation, or enabling self-serve analytics workflows
• The ability to take ambiguous requirements, define the right data model and pipeline design, and deliver with minimal oversight

Benefits

• Professional development budget
• Competitive benefits that cover physical and mental healthcare, retirement, family forming, and family support
• Employee giving match
• Mobile phone stipend
• R&R days
• Generous wellness reimbursement and weekly onsite & virtual programming
• Generous vacation policy
• Industry-leading parental leave policies and family planning benefits
• Catered lunches, fully-stocked kitchens with premium snacks/beverages