Risk Data AI/ML Engineer

<div class="content-intro"><p><a href="https://www.sofi.com/sofi-employee-applicant-privacy-notice/" target="_blank"><strong>Employee Applicant Privacy Notice</strong></a></p> <p><strong>Who we are:</strong></p> <div> <p>Shape a brighter financial future with us.</p> <p>Together with our members, we’re changing the way people think about and interact with personal finance.</p> <p>We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. <strong>Join us to invest in yourself, your career, and the financial world.</strong></p> </div></div><h2><strong>The role:</strong></h2> <p>We are seeking a Senior Data Engineer to join our Risk Data Team as a hands-on technical lead supporting Credit, Collections, and Fraud. This role blends deep production data engineering with formal technical and people leadership.</p> <p>You will own architectural decisions for the Risk data platform, define modeling standards, elevate engineering rigor, and build scalable data systems that power risk decisioning across the organization. This role exists to ensure that Risk data pipelines are reliable, well-modeled, observable, and built with long-term maintainability in mind.</p> <p>You will contribute directly to production data pipelines while setting standards for data modeling, dbt architecture, code quality, and observability. This is not an architect-only or strategy-only role — it requires hands-on execution and demonstrated team leadership ownership.</p> <h2><strong>What you’ll do:</strong></h2> <h3><strong>Technical Leadership</strong></h3> <ul> <li>Serve as technical lead for the Risk Data Engineering team.</li> <li>Own architectural decisions and data modeling strategy across the Risk domain.</li> <li>Define naming conventions, modeling standards, and layered dbt architecture (staging → intermediate → marts).</li> <li>Lead architecture discussions and technical planning sessions.</li> <li>Conduct code reviews focused on maintainability, readability, and long-term scalability.</li> <li>Translate business priorities into well-scoped, production-ready technical deliverables.<br><br></li> </ul> <h3><strong>Production Data Engineering</strong></h3> <ul> <li>Design and build production-grade Snowflake data models.</li> <li>Develop scalable dbt projects, including reusable macros and testing frameworks.</li> <li>Manage Apache Airflow DAGs, including idempotency, retry logic, and failure handling.</li> <li>Implement CI/CD best practices for dbt and data pipelines.</li> <li>Drive automation initiatives to reduce manual operational overhead.<br><br></li> </ul> <h3><strong>Data Modeling</strong></h3> <ul> <li>Design dimensional and relational models aligned to business definitions.</li> <li>Apply modeling best practices including grain declaration, SCD strategies, and surrogate key management.</li> <li>Balance normalization and performance trade-offs.</li> <li>Evolve models safely as business requirements change.</li> <li>Ensure all models are clearly documented with lineage and business logic.<br><br></li> </ul> <h3><strong>Data Quality Observability</strong></h3> <ul> <li>Own the dbt testing framework (schema tests, custom tests, generic tests).</li> <li>Define and enforce freshness checks, SLA standards, and row-count validations.</li> <li>Implement monitoring and observability using DataDog.</li> <li>Proactively identify and reduce reliability incidents.</li> <li>Establish measurable data quality SLAs in partnership with stakeholders.<br><br></li> </ul> <h3><strong>People Leadership</strong></h3> <ul> <li>Participate in hiring, onboarding, and team building.</li> <li>Run regular 1:1s and provide structured ... (truncated, view full listing at source)