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Ripple is building financial infrastructure for a world where value moves like information. As a Senior Software Engineer on the Data Engineering team, you will be a core builder of the Caspian Data Platform—Ripple's centralized lakehouse powering analytics, financial reporting, product intelligence, and data-driven operations across all business units.
You will own the design and delivery of production-grade ETL pipelines end-to-end, primarily on Databricks using Delta Live Tables and Spark with medallion architecture patterns. Your responsibilities include designing and maintaining data models across Finance, Payments, Product, and GTM domains, ensuring they serve as reliable sources of truth for business metrics. You will drive technical design for cross-functional projects, write clear design documents articulating architecture and tradeoffs, and build observability into pipelines through failure detection, alerting, and retry logic.
You will integrate AI tooling into data workflows—agentic systems for pipeline triage, automated code generation for transformations, and natural language interfaces for data access. You'll mentor junior engineers through code reviews and design feedback, helping them grow into more complex ownership. You will contribute to platform infrastructure using Terraform and work with Unity Catalog to establish data contracts, access controls, and lineage.
You bring 5–8 years of hands-on data engineering experience building and maintaining production pipelines and data models. You have deep proficiency with Databricks (Delta Live Tables, Unity Catalog, Delta Lake) and strong SQL skills for complex transformations, incremental materialization, and schema evolution. You are proficient in Python for data engineering tasks, have solid AWS experience, and are comfortable with CI/CD tooling (GitLab) and infrastructure-as-code (Terraform). Experience with AI tooling in data contexts and familiarity with financial, payments, or blockchain data are valued. You operate with high autonomy, take ambiguous requirements and define the right data model and pipeline design with minimal oversight.
