Distinguished Software Engineer, Data Platform - Saviynt

Saviynt's AI-powered identity platform manages and governs human and non-human access to all of an organization's applications, data, and business processes. Customers trust Saviynt to safeguard their digital assets, drive operational efficiency, and reduce compliance costs. Built for the AI age, Saviynt is today helping organizations safely accelerate their deployment and usage of AI. Saviynt is recognized as the leader in identity security, with solutions that protect and empower the world’s leading brands, Fortune 500 companies and government institutions.

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Our platform serves hundreds of enterprise and government tenants, and much of the data that powers it is still locked inside isolated, per-customer systems that our newer multi-tenant applications cannot reach. As Distinguished Engineer, Data Platform, you will own the end-to-end vision, architecture, and execution for solving this at the highest level: a single, governed data platform that ingests data out of legacy per-tenant systems in real time, transforms and serves it through one shared, multi-tenant layer, and lets every consuming application — operational, analytical, and AI-driven — build on it through standard, self-service access rather than one-off integrations.

This is a multi-year, org-wide mandate. You will set the technical direction that other principal and senior engineers build against, defend it in front of executive and security stakeholders, and see it through from first proof point to company-wide standard.

• Own the long-term architectural vision for unifying data spread across hundreds of isolated,
• Define the target end-state architecture — ingestion, transformation, storage, and access —
• Set the technical standards — schema governance, data contracts, tenant isolation models —
• Architect a real-time data-replication strategy that moves data out of legacy, single-tenant
• Design the underlying streaming and integration backbone as a shared, multi-tenant, multi-
• Define the long-term path toward bidirectional integration, so applications can eventually act
• Design a layered data architecture — raw ingestion, domain-specific transformation, and a
• Build a unified data access layer, supporting both synchronous and asynchronous consumption, that enforces authorization and eliminates direct, ungoverned access to underlying data stores.
• Enable every current and future consuming application — operational, analytical, and AI driven — to onboard onto the platform through standard, self-service integration paths.
• Extend the platform's governed data layer to serve as the foundation for machine-learning and generative-AI use cases, not just reporting and analytics.