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

SoFi • San Francisco, CA

2d

About The Position

SoFi is hiring a Principal Software Engineer in its Data Platform Group to provide hands-on technical leadership within the Data Trust pillar. The role designs, builds, and operates distributed systems and reusable platform primitives that make data quality, privacy, metadata, lineage, access, and reliability enforceable across the enterprise. It also establishes a governed semantic context layer for trusted analytics and AI by making business meaning, ownership, provenance, quality, freshness, and access policies machine-readable.

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

The role’s first 12–18 months are expected to establish a clear architecture and delivery path for Data Trust primitives; improve coverage, observability, SLOs, and automated enforcement in priority data domains; deliver production-ready governance patterns for batch and low-latency environments; put initial semantic context capabilities into production for analytics and AI; reduce manual investigation, recurring failures, and compliance evidence-gathering; and encourage adoption of shared standards rather than isolated solutions.

Requirements

• 15+ years of software or data engineering experience, or 13+ years with a Master’s degree.
• Proven track record designing, building, and operating distributed data platforms at enterprise scale.
• Deep experience designing horizontal, multi-tenant platform services used by other engineering teams.
• Understanding of distributed-system failure modes and eventual consistency, and of balancing latency, availability, and cost.
• Practical experience building or managing modern data trust components such as data contracts, metadata catalogs, data quality frameworks, end-to-end lineage, and fine-grained access control (RBAC/ABAC).
• Advanced knowledge of modern data ecosystems, specifically Snowflake, Kafka or similar streaming platforms, and AWS data services.
• Mastery of one or more core programming languages, such as Python, Java, or Go, and advanced SQL.
• Ability to write production-grade code, conduct rigorous code reviews, and build declarative infrastructure using tools and practices such as Terraform and CI/CD.
• History of driving multi-team, cross-functional technical initiatives and building consensus across Engineering, Security, Privacy, and Product teams.
• Ability to translate compliance and risk requirements into engineering primitives.
• Experience managing Tier-0 or mission-critical infrastructure.
• Use of automated testing, drift detection, and circuit breakers rather than reliance on manual processes.

Nice To Haves

• Prior experience in FinTech or another highly regulated environment where auditability, data privacy (CCPA/GDPR), and data integrity are architectural requirements.
• Experience building semantic layers, knowledge graphs, or context engines that ground LLMs and autonomous agents in governed enterprise data.
• Contributions to relevant open-source data or metadata projects, such as Apache Iceberg, OpenMetadata, DataHub, or Amundsen.

Responsibilities

• Define and execute 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.
• 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.
• Establish technical foundations for a governed semantic context layer connecting business definitions, metadata, lineage, ownership, quality, freshness, and access policy to support trusted discovery, analytics, and AI-assisted or agentic experiences.
• 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.
• Improve production data systems through observability, integrity checks, audit evidence, incident learning, and safe automation; advance proactive, measurable controls in place of reactive troubleshooting and manual compliance activity.
• Drive multi-quarter initiatives across teams and functions, building alignment on architectural tradeoffs and investment decisions.
• Serve as a trusted technical authority for Data Trust, mentor senior engineers, strengthen design and implementation practices, and raise engineering standards.
• Contribute code, prototypes, architecture, and operational improvements while developing reusable enterprise capabilities and reference patterns for partner teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal