As a Principal Software Engineer in the Data Platform Group, you will provide hands-on technical leadership within the Data Trust pillar.

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.

Responsibilities:

• 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.
• 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.
• 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.
• Mission-critical operational rigor: Raise the standard for 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, earning alignment on difficult architectural tradeoffs and investment decisions.

What success looks like in the first 12–18 months:

• 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.

Requirements:

• 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.
• Deep experience designing horizontal, multi-tenant platform services that other engineering teams depend on.
• 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).
• 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 (e.g., Python, Java, Go) and advanced SQL.
• A history of successfully driving multi-team, cross-functional technical initiatives.
• Experience managing Tier-0/mission-critical infrastructure.

Nice to have:

• Prior experience operating in a highly regulated environment where auditability, data privacy (CCPA/GDPR), and data integrity are architectural requirements, not afterthoughts.
• 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 (e.g., Apache Iceberg, OpenMetadata, DataHub, Amundsen).

Compensation and Benefits:

The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.

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You will work with the AI Platform engineering team, product leader, and subject-matter experts across the company. You will help departments reimagine how their work gets done, build the first workflows that make that future real, and turn what you learn in the field into durable platform capabilities.

Because you will be the first person in this role, you will also define what forward deployment means at SoFi: how we discover opportunities, onboard departments, build and evaluate workflows, measure outcomes, support users, and translate repeated needs into platform investments.

Your work will establish the technical and operating foundation for a broader AI enablement capability. This role does not have a playbook. It is an opportunity to write one and to define how applied AI changes the way work gets done across SoFi.

Responsibilities

• Operate as a product-minded engineer, platform architect, and hands-on builder.
• Partner with leaders and subject-matter experts across SoFi to identify high-value opportunities to transform work with AI.
• Embed with internal departments to understand their workflows, systems, data, constraints, and measures of success and deploy AI-enabled workflows that solve real business problems.
• Lead departments from initial discovery through onboarding, adoption, production use, and measurable outcomes.
• Develop the playbooks, technical patterns, and operating practices that allow AI enablement to scale across the company.
• Build reusable skills, tools, integrations, agents, and workflow components on top of SoFi's internal AI platform.
• Contribute directly to the platform when field deployments reveal missing capabilities or opportunities to improve the developer and user experience.
• Create tight feedback loops among users, AI Enablement, product, and platform engineering.
• Serve as a trusted technical advisor to senior business and technology leaders navigating the opportunities and tradeoffs of applied AI.

Requirements

• Deep experience building and operating sophisticated software systems at Sr Staff Engineer equivalent scope.
• A track record of leading technically ambiguous, cross-functional initiatives from problem definition through production adoption.
• Strong product judgment and an instinct for finding the real problem behind a feature request.
• Experience designing applications, platforms, developer tools, workflow systems, or distributed systems.
• Practical experience building with large language models, including tool use, context engineering, agents, evaluations, and human-in-the-loop patterns.
• The judgment to distinguish among a prototype, a department-specific solution, and a capability that belongs in a shared platform.
• Exceptional written and verbal communication skills across technical and nontechnical audiences.
• Comfort creating structure, standards, and momentum in a space where the operating model is still being formed.

Nice to Have

• Experience as a founding or early Forward Deployed Engineer, product engineer, product manager, solutions architect, or technical leader for an enterprise platform.
• Experience building an internal developer platform, AI platform, enterprise productivity product, or agentic system.
• Experience building evaluation and feedback systems for nondeterministic or AI-enabled products.
• Experience operating in financial services or another highly regulated environment.

What Success Looks Like:

• Established a repeatable model for taking departments from opportunity discovery to successful production use.
• Delivered AI-enabled workflows that create measurable improvements in speed, quality, capacity, or employee experience.
• Built a trusted partnership among internal users, AI Enablement, and the AI Platform organization.
• Converted insights from early deployments into reusable capabilities within the shared platform.
• Defined standards for onboarding, workflow development, evaluation, deployment, support, and continuous improvement.
• Created the technical and operating foundation from which SoFi can scale its Forward Deployed Engineering and AI Enablement capabilities.

Compensation and Benefits The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate's experience, skills, and location.

Job Data by YubHub.

You will lead end-to-end delivery and architecture for enterprise security capabilities: enterprise zero-trust networks, endpoint security, and SaaS posture management.

Your responsibilities will also include:

• Framing complex security challenges: clarifying security requirements, threat models, failure modes, and success metrics while proposing clear architectural options and tradeoffs.
• Designing for scale and resilience: establishing secure baseline configurations, automating security guardrails, and reducing systemic attack surfaces with minimal enterprise friction.
• Collaborating with internal security teams to establish and maintain threat signaling to ensure that proper visibility and alerting is achieved across tooling.
• Raising cybersecurity standards through design reviews, active mentorship of team members, and establishing enterprise security patterns and best practices.
• Collaborating cross-functionally with IT, Infrastructure, Risk/Compliance, and Engineering teams to deliver secure member outcomes.

To qualify, you will need:

• A Bachelor’s Degree + 3–4 Years of Experience OR Master’s Degree + 2–3 Years of Experience in Cybersecurity, Computer Science, Information Technology, or a related field.
• 3+ years of experience in cybersecurity with a focus on Data Loss Prevention (DLP).
• Hands-on experience with cloud-based DLP solutions (e.g., Zscaler, Proofpoint, Sentra).
• Experience administering and operating core enterprise security tools (EDR, email security gateways, and web proxies).
• Experience with Infrastructure as Code (IaC) solutions such as Terraform, along with scripting languages (Python, Go, or PowerShell) for tooling and automation.
• Experience with incident response, log analysis, threat detection, and digital forensics.
• Experience collaborating with multiple lines of defense for issue analysis, evidence gathering and remediation.
• Proven problem-solving skills with the ability to work independently and collaboratively in a fast-paced environment.
• Solid written and verbal communication skills for technical documentation, cross-functional collaboration, and threat modeling.

Nice to have:

• Professional security certifications (e.g., CISSP, CCSP, CISM, or AWS Certified Security Specialty).
• Experience with CI/CD security pipeline automation, SIEM detections, and SOAR playbooks.
• Knowledge of financial compliance frameworks and regulatory environments (PCI-DSS, SOC1/SOC2, SOX, GLBA, NIST CSF, ISO 27001).

The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.

Job Data by YubHub.