Principal Software Engineer, Data Platform — Data Trust & AI Context

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.

• →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 automation. Help move the organization from reactive troubleshooting and manual compliance activity toward proactive, measurable control.
• →Cross-organizational technical leadership: Drive multi-quarter initiatives across teams and functions, earning alignment on difficult architectural tradeoffs and investment decisions. Serve as a trusted technical authority for Data Trust while mentoring senior engineers, strengthening design and implementation practices, and raising the broader engineering bar.

• A clear enterprise architecture and delivery path exists for Data Trust primitives spanning contracts, metadata, lineage, quality, privacy, and reliability.
• Priority data domains have materially improved coverage, observability, SLOs, and automated enforcement.
• The platform provides production-ready patterns for governing data in both batch and low-latency environments.
• Initial semantic context capabilities are operating in production and provide more reliable, traceable context for analytics and AI use cases.
• Manual investigation, recurring data failures, and compliance evidence-gathering are measurably reduced.
• Engineering and partner teams have adopted reusable standards and capabilities rather than creating isolated point solutions.

• Experience & Education: 15+ years of software or data engineering experience (or 13+ years with a Master’s degree) with a proven track record of designing, building, and operating distributed data platforms at enterprise scale.
• Platform Architecture Expertise: Deep experience designing horizontal, multi-tenant platform services that other engineering teams depend on. You understand the failure modes of distributed systems, eventual consistency, and how to balance latency, availability, and cost.
• Data Trust & Governance Depth: Practical, battle-tested experience building or managing components of the modern data trust stack, such as data contracts, metadata catalogs, data quality frameworks, end-to-end lineage, and fine-grained access control (RBAC/ABAC).
• Batch & Streaming Mastery: Advanced knowledge of modern data ecosystems, specifically Snowflake, Kafka (or similar streaming platforms), and AWS data services. You know how to govern data in motion just as effectively as data at rest.
• Hands-On Engineering: Mastery of one or more core programming languages (e.g., Python, Java, Go) and advanced SQL. You are comfortable writing production-grade code, conducting rigorous code reviews, and building declarative infrastructure (Terraform, CI/CD).
• Cross-Organizational Leadership: A history of successfully driving multi-team, cross-functional technical initiatives. You know how to build consensus across Engineering, Security, Privacy, and Product teams, translating compliance and risk requirements into frictionless engineering primitives.
• Operational Rigor: Experience managing Tier-0/mission-critical infrastructure. You build systems with a "trust but verify" mindset—relying on automated testing, drift detection, and circuit breakers rather than manual processes.

• FinTech / Regulated Industry Experience: Prior experience operating in a highly regulated environment where auditability, data privacy (CCPA/GDPR), and data integrity are architectural requirements, not afterthoughts.
• AI Infrastructure Exposure: Experience building semantic layers, knowledge graphs, or context engines that ground LLMs and autonomous agents in governed enterprise data.
• Open Source Contributions: Contributions to relevant open-source data or metadata projects (e.g., Apache Iceberg, OpenMetadata, DataHub, Amundsen).