Senior Manager, Data Engineering

The role:

SoFi is looking for a Senior Manager, Data Engineering to join our IRM Analytics team. This is a hands-on leadership role (50% individual contributor, 50% people manager) for a seasoned data engineering leader with deep roots in US banking and financial risk data.

You will own the data models, data pipelines, and data infrastructure that underpin our risk and AI applications - spanning critical domains like lending, credit, fraud, AML, and compliance. You will lead a focussed team of data engineers, set technical direction, and be an integral contributor to every data and AI initiative we build.

This role is for someone who has spent most of their career inside US banks and understands not just the technology, but the data - the domains, the regulatory context, the critical datasets, and why they matter.

What you’ll do:

• Own the data model - design, build, and maintain integrated data models for lending, credit, fraud, AML, KYC, and related risk domains; ensure models reflect banking semantics and regulatory requirements
• Own the data pipeline and infrastructure - architect and manage end-to-end data pipelines using dbt, Airflow, Snowflake, MongoDB, and Terraform; ensure reliability, performance, and scalability
• Lead data and AI projects - serve as the data engineering anchor for all data and AI initiatives; partner with full stack engineers, AI/ML engineers, and product managers to deliver production-grade applications; own the data infrastructure for AI use cases including RAG pipeline data management and evaluation dataset / ground truth curation
• Lead a team of data engineers - manage and develop the team; set standards for code quality, code review, testing, documentation, and CI/CD practices for data pipelines
• Drive data quality and governance - establish data definitions, lineage, and quality standards aligned to regulatory expectations (BCBS 239, SR 11-7, etc.); implement data observability practices including dbt tests, data contracts, freshness SLAs, and anomaly detection to ensure reliability across all pipelines
• Stay 50% hands-on - write and review production code, own critical pipelines, and lead by example

What you’ll need:

• 15+ years of experience in data engineering, with the majority of that tenure inside US banks or financial institutions
• Deep knowledge of banking risk data domains - lending, credit risk, deposits, AML, KYC, fraud, banking regulations, and the critical datasets that support them
• Expert-level Snowflake, dbt, and Python - proven ability to design and own complex analytical data models; Python fluency for Airflow DAGs, pipeline logic, and data quality scripting
• Strong pipeline and infrastructure skills - hands-on experience with Airflow, MongoDB, and Terraform in production environments
• People leadership - experience managing and mentoring data engineers; strong code review culture
• Banking regulatory awareness - familiarity with BCBS 239, BSA/AML regulations, OCC/Fed/FDIC data expectations
• Communication - able to translate complex data concepts for risk, compliance, and executive stakeholders

Nice to have:

• Experience at a US bank in Model Risk, Integrated Risk, ERM, or Compliance Analytics
• Familiarity with GRC platforms (ServiceNow) and risk data warehouses
• Experience with LLM/AI application data pipelines and observability tooling