Staff Risk AI & Data Engineer

SoFi is a next-generation financial services company and national bank focused on transforming personal finance through innovative technology. They are seeking a Staff Risk AI & Data Engineer to lead engineering teams, drive technical excellence, and implement AI-enabled engineering practices across the Risk Data organization.

Responsibilities:

• Lead, mentor, calibrate, and upskill a high-performing pod of data engineers while raising the technical and operational bar across the broader Risk Data organization
• Conduct regular skills assessments, identify development gaps, and create targeted growth plans to strengthen engineering capabilities and support long-term succession planning
• Establish measurable ownership frameworks, delivery KPIs, and operational standards that improve accountability, scalability, and engineering quality
• Champion AI-native engineering workflows across the organization, including AI-assisted development environments, intelligent alerting and triage systems, automated documentation, and AI-powered operational tooling
• Standardize and optimize engineering and data delivery processes across multiple teams to improve predictability, scalability, and delivery efficiency
• Design and implement scalable frameworks, templates, and standards across onboarding, incident response, semantic layers, dbt modeling, observability, governance, and data validation practices
• Provide technical leadership across Snowflake platform architecture, dbt transformation strategy, Airflow orchestration, AI-enabled workflows, and enterprise-scale data platform governance
• Review and challenge technical strategies, pipeline architectures, and implementation plans to ensure long-term scalability, reliability, compliance, and operational maturity
• Ensure strong governance, security, lineage, observability, and compliance standards across production data systems and AI-enabled workflows
• Serve as the primary technical liaison for strategic vendor and platform relationships, including Snowflake, credit bureau providers, governance tooling vendors, and AI/data platform partners
• Partner with vendors on platform optimization, feature adoption, integration strategy, SLA management, and operational scalability initiatives
• Collaborate closely with platform engineering, infrastructure, compliance, analytics, and model risk teams to align Risk Data initiatives with broader enterprise strategy
• Evaluate emerging AI and data technologies, including AI-powered ETL, intelligent monitoring systems, and LLM-enabled operational tooling, and make strategic build-versus-buy recommendations
• Partner with Product Managers and cross-functional stakeholders to translate complex business and Risk requirements into scalable engineering roadmaps and technical solutions
• Clearly communicate operational health, delivery metrics, technical risks, and engineering strategy to technical and non-technical leadership stakeholders

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field
• 8+ years of experience in data engineering, platform engineering, or enterprise-scale data infrastructure environments
• 3+ years of experience leading engineering teams, technical pods, or cross-functional technical initiatives
• Deep hands-on expertise with Snowflake, including platform architecture, optimization, governance, performance tuning, and enterprise-scale data platform management
• Strong experience with dbt, including transformation frameworks, semantic layer strategy, testing, and scalable data modeling practices
• Strong programming experience with Python, SQL, and modern data engineering patterns
• Experience with Apache Airflow and production-grade orchestration workflows
• Demonstrated experience leveraging AI tools and AI-assisted workflows to improve engineering productivity, operational efficiency, monitoring, documentation, or incident response
• Strong understanding of governance, observability, compliance, lineage, and access-control best practices across enterprise data environments
• Proven ability to mentor engineers, influence technical direction, drive organizational standards, and scale engineering excellence across teams
• Strong communication and stakeholder management skills with experience partnering across engineering, product, compliance, infrastructure, analytics, and vendor organizations
• Experience within financial services, fintech, lending, or risk management environments
• Familiarity with RAG systems, intelligent agents, or LLM-powered operational tooling
• Experience implementing AI-assisted SDLC or AI-native engineering workflows at scale
• Experience with enterprise governance, lineage, or observability tooling
• Experience managing strategic vendor or platform partnerships
• Familiarity with CI/CD, cloud-native infrastructure, and distributed systems architecture