Staff Risk AI & Data Engineer

The role:

We are seeking a Staff Risk AI & Data Engineer to lead a high-performing engineering pod and drive technical excellence, operational maturity, and AI-enabled engineering practices across the broader Risk Data organization.

This is a senior technical leadership role focused on scaling modern data platforms, AI-native engineering workflows, and organizational standards across multiple teams. You will lead through technical strategy, process optimization, team development, and scalable framework creation while partnering closely with engineering leadership, product, compliance, infrastructure, analytics, and external vendors.

The ideal candidate combines strong leadership and team calibration experience with deep expertise across Snowflake, dbt, Python, Airflow, and modern AI-enabled engineering workflows. This role requires a hands-on technical leader who can challenge architectural decisions, improve operational efficiency, and help shape the long-term direction of SoFi’s Risk Data ecosystem.

What you’ll do:

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.

What you’ll need:

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.

Nice to have:

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.