Principal Software Engineer, Data Platform — Data Trust & AI Context
SoFi · WA - Seattle; UT - Cottonwood Heights; CA - San Francisco
About this role
**Employee Applicant Privacy Notice** **Who we are** Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. SoFi is a next-generation financial services company and national bank using innovative, mobile-first technology to help millions of members reach their goals. **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’s needed. As a **Principal Software Engineer** in the **Data Platform Group**, you’ll provide hands-on technical leadership within the **Data Trust** pillar. You’ll design, build, and operate distributed systems and reusable platform primitives that make **data quality, privacy, metadata, lineage, access, and reliability** enforceable by construction across the enterprise. Your work spans three connected areas: - Establishing shared **Data Trust** capabilities - Raising **reliability** of data across batch and low-latency environments - Applying those foundations to a **semantic context layer** for trusted analytics and AI This is an **individual contributor** role with broad organizational impact. You’ll contribute code, prototypes, architecture, and operational improvements—while setting technical direction across engineering, data, security, privacy, legal, risk, analytics, and product teams. **What you will own** - **Data Trust architecture and strategy:** Define and execute technical direction for data contracts, metadata/catalog modernization, lineage, data quality, privacy, and access controls. - **Declarative enforcement primitives:** Build platform services, libraries, and reference patterns to make contracts, quality rules, privacy policies, and access requirements versioned, testable, observable, and automatically enforceable. - **Semantic context for analytics and AI:** Establish foundations for a governed semantic context layer connecting business definitions, metadata, lineage, ownership, quality, freshness, and access policy. - **Reliability across batch and streaming data:** Lead evolution of Data Trust capabilities across Snowflake, AWS data services, Kafka-based ingestion, and other environments. Define SLOs, failure modes, correction paths, and operational ownership with automated detection, containment, remediation, and recovery. - **Mission-critical operational rigor:** Improve production data systems through observability, integrity checks, audit evidence, incident learning, and safe automation. - **Cross-organizational technical leadership:** Drive multi-quarter initiatives across teams and functions; mentor senior engineers and raise the engineering bar. **What success looks like in the first 12–18 months** - Clear enterprise architecture and delivery path for Data Trust primitives (contracts, metadata, lineage, quality, privacy, reliability) - Improved coverage, observability, SLOs, and automated enforcement for priority data domains - Production-ready patterns for governing data in both batch and low-latency environments - Semantic context capabilities operating in production with more reliable, traceable context for analytics and AI - Measurable reduction in manual investigation, recurring data failures, and compliance evidence gathering - Adoption of reusable standards/capabilities by engineering and partner teams (fewer isolated point s
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