Risk Data AI/ML Engineer

SoFi is a next-generation financial services company and national bank that is changing the way people think about personal finance. They are seeking a Senior Data Engineer to join their Risk Data Team, where the individual will lead technical efforts in building and maintaining data systems that support risk decision-making across the organization.

Responsibilities:

• Serve as technical lead for the Risk Data Engineering team
• Own architectural decisions and data modeling strategy across the Risk domain
• Define naming conventions, modeling standards, and layered dbt architecture (staging → intermediate → marts)
• Lead architecture discussions and technical planning sessions
• Conduct code reviews focused on maintainability, readability, and long-term scalability
• Translate business priorities into well-scoped, production-ready technical deliverables
• Design and build production-grade Snowflake data models
• Develop scalable dbt projects, including reusable macros and testing frameworks
• Manage Apache Airflow DAGs, including idempotency, retry logic, and failure handling
• Implement CI/CD best practices for dbt and data pipelines
• Drive automation initiatives to reduce manual operational overhead
• Design dimensional and relational models aligned to business definitions
• Apply modeling best practices including grain declaration, SCD strategies, and surrogate key management
• Balance normalization and performance trade-offs
• Evolve models safely as business requirements change
• Ensure all models are clearly documented with lineage and business logic
• Own the dbt testing framework (schema tests, custom tests, generic tests)
• Define and enforce freshness checks, SLA standards, and row-count validations
• Implement monitoring and observability using DataDog
• Proactively identify and reduce reliability incidents
• Establish measurable data quality SLAs in partnership with stakeholders
• Participate in hiring, onboarding, and team building
• Run regular 1:1s and provide structured performance feedback
• Develop engineers toward ownership and technical growth
• Address underperformance early and constructively
• Foster a culture of accountability, documentation, and engineering excellence
• Partner with Risk Data Product Managers, Data Science, ML, and business stakeholders
• Communicate modeling decisions, trade-offs, and pipeline health clearly
• Influence cross-functional technical direction across Risk and platform teams
• Maintain scalable, secure data systems aligned with enterprise governance standards
• Improve documentation practices including runbooks and architecture decision records
• Contribute to workforce planning and technical roadmap discussions

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field (or equivalent work experience)
• 8+ years of hands-on data engineering experience
• 2+ years of experience serving as a tech lead or leading engineers formally
• Deep expertise in dimensional and relational data modeling, including SCD strategies and grain design
• Advanced dbt experience, including layered architecture, macros, advanced testing, and semantic layer concepts
• Strong hands-on Snowflake experience, including modeling and performance optimization
• Production-level experience managing Apache Airflow DAGs
• Advanced SQL skills, including query optimization and performance tuning
• Strong Python skills for data pipeline development and automation
• Demonstrated ownership of a data quality and monitoring framework
• Experience working in regulated or high-accuracy environments
• Experience participating in hiring, onboarding, and performance management
• Strong communication skills and ability to influence cross-functional stakeholders
• Experience with Snowflake advanced capabilities (Snowpark, Cortex AI, ML functions)
• Familiarity with LLM tooling, RAG systems, or AI-assisted data workflows
• Financial services experience (Credit, Fraud, Collections)
• AWS experience (S3, Glue, Lambda) and infrastructure-as-code familiarity
• Experience implementing data governance frameworks at scale