Staff Software Engineer - Product Foundations

Network Foundations is Plaid’s authoritative source of truth for the user lifecycle, powering user recognition across integration paths, authentication with Plaid, intelligent and context-aware onboarding flows, and the core user data models that drive insights for Plaid’s newest ML-based products.

We own the living graph of people’s financial lives — and we operate it at global scale for some of the world’s largest companies, including Google, Meta, Shopify, Square, Robinhood, and Venmo.

Our mission is to make Plaid’s network data easy to query, highly accurate, and legally compliant. The infrastructure we build is used by product and machine learning teams across the company to develop high-precision features, analytics, and customer experiences. We focus on enabling consistent data access across both real-time and offline workflows.

Responsibilities:
• Design and build backend data systems that make it possible to query a user’s complete financial life (Plaid users, accounts, and transactions, identity) at scale. You will develop and maintain graph-based infrastructure for identity resolution and entity mapping
• Lead high-impact projects from design through execution: your work will touch tens of millions of end-users, the best applications in fintech, and major financial institutions. You will deliver APs and datasets that power fraud detection, credit decisioning, and personal finance insights
• Work on both the 0 to 1 stage and the 1 to n stage of problems
• Collaborate with data science, machine learning, legal and product teams to support feature development and analytics
• Establish best practices for data quality, performance, reliability and explainability
• Contribute to the team’s technical roadmap and strategy, mentor engineers, and help grow a culture of excellence

Qualifications:
• 8+ years of software engineering experience, including backend system design and data infrastructure
• Proven experience designing and maintaining distributed systems at scale
• Strong programming skills in Go, Python, or similar backend languages
• Experience working with data platforms (e.g., Redshift, Kafka, Airflow, DBT, or equivalent)
• Familiarity with data modeling and lifecycle challenges
• Strong communication and collaboration skills with cross-functional partners

Nice-to-Haves:
• Experience with graph databases or graph-based data modeling
• Exposure to ML infrastructure or support systems (e.g., feature stores, batch/stream data)
• Understanding of data privacy, data access restrictions, or legal compliance in data systems
• Prior experience mentoring or leading technical direction for other engineers
• Experience defining the roadmap of an ambiguous technical area.