Principal Software Engineer, Data Platform — Data Trust & AI Context WA - Seattle; UT - Cottonwood Heights; CA

Shape a brighter financial future with us.

Together with our members, we’re changing the way people think about and interact with personal finance.

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.

Join us to invest in yourself, your career, and the financial world.

The Role:

SoFi’s ability to deliver trusted member experiences, manage risk, meet regulatory obligations, and scale enterprise AI depends on data that is reliable, governed, understandable, and available when it is needed.

As a Principal Software Engineer in the Data Platform Group, you will provide hands-on technical leadership within the Data Trust pillar. Working alongside the Senior Manager and other Principal Engineers, you will design, build, and operate the distributed systems and reusable platform primitives that make data quality, privacy, metadata, lineage, access, and reliability enforceable by construction across the enterprise.

Your work will span three connected areas: establishing shared Data Trust capabilities, raising the reliability of data across batch and low-latency environments, and applying those foundations to a semantic context layer for trusted analytics and AI. By making business meaning, ownership, provenance, quality, freshness, and access policies machine-readable, you will help end users and AI systems work from consistent, governed context.

This is an individual contributor role with broad organizational impact. You will personally contribute code, prototypes, architecture, and operational improvements while setting technical direction across engineering, data, security, privacy, legal, risk, analytics, and product teams. You will build reusable enterprise capabilities and reference patterns that partner teams can adopt, while domain teams retain ownership of their application-specific use cases and serving paths.

What you will own:

• Data Trust architecture and strategy: Define and execute the technical direction for data contracts, metadata and catalog modernization, lineage, data quality, privacy, and access controls. Translate business, regulatory, and engineering requirements into a coherent platform strategy and sequence investments for long-term impact.
• Declarative enforcement primitives: Build platform services, libraries, and reference patterns that make contracts, quality rules, privacy policies, and access requirements versioned, testable, observable, and automatically enforceable across the data lifecycle.
• Semantic context for analytics and AI: Establish the technical foundations for a governed semantic context layer that connects business definitions, metadata, lineage, ownership, quality, freshness, and access policy. Enable trusted discovery, analytics, and AI-assisted or agentic experiences without turning this role into a model-development function.
• Reliability across batch and streaming data: Lead the evolution of Data Trust capabilities across Snowflake, AWS data services, Kafka-based ingestion, and other batch or low-latency environments. Establish explicit SLOs, failure modes, correction paths, and operational ownership, supported by automated detection, containment, remediation, and recovery.
• Mission-critical operational rigor: Raise the standard for production data systems through observability, integrity checks, audit evidence, incident learning, and safe…