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

SoFi, WA - Seattle +2

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.

All jobs

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

SoFi, WA - Seattle +2

Posted pay

Pay not posted

Location: WA - Seattle +2
Work type: Not given
Level: Principal
Experience: 15+ years
Skills in the job text: Python
AWS
SQL
Java
Go
Terraform
Kafka
Snowflake
Posted: 18 days ago
Last check: 9 h ago
Job type: Infra, platform & SRE, Data engineering

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SoFi

Open tech jobs: 16
With posted pay: 0%

About SoFi: funding, hiring and pay

SoFi

About SoFi: funding, hiring and pay

Company facts

Money raised: At least $1.1BSum of SEC filings. A filing does not show all money that a company raises.
Newest raise: Mar 3, 2022First sale on an SEC filing

Funding records can have errors and can be missing. The company page has the links.

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The role

What you will do

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 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.

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.

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

The role

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

The role

What you will do

What you will own

What success looks like in the first 12–18 months

What you need

Nice to have

Pay and benefits

About SoFi

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SoFi