Distinguished Software Engineer, Data Platform

Multi-Tenant Data Platform Architecture & Vision • Own the long-term architectural vision for unifying data spread across hundreds of isolated, per-tenant systems into one governed, multi-tenant data platform. • Define the target end-state architecture — ingestion, transformation, storage, and access — and the phased roadmap to get there, starting from the first application's needs and scaling to serve the entire portfolio. • Set the technical standards — schema governance, data contracts, tenant isolation models — that every consuming team builds against.

Real-Time Data Movement; Integration • Architect a real-time data-replication strategy that moves data out of legacy, single-tenant systems into the platform with minimal latency, without requiring source application teams to re-architect. • Design the underlying streaming and integration backbone as a shared, multi-tenant, multi- consumer capability — not a one-off pipeline built per application. • Define the long-term path toward bidirectional integration, so applications can eventually act on platform data through governed, auditable pathways — not just read it.

Common Data Platform: Ingestion, Transformation & Governed Access • Design a layered data architecture — raw ingestion, domain-specific transformation, and a governed serving layer — that lets each consuming team own its own data model within shared guardrails, instead of building its own pipeline. Own the datastore strategy for the platform's mixed transactional and analytical workloads, running rigorous, benchmark-backed evaluations and defending the resulting recommendation to executive and security stakeholders. • Build a unified data access layer, supporting both synchronous and asynchronous consumption, that enforces authorization and eliminates direct, ungoverned access to underlying datastores.

Multi-Tenant Consumption at Scale • 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. • Establish per-tenant cost visibility and quota governance so the platform scales economically as consumption grows across teams and tenants.

Observability, Governance & Compliance • Build observability, lineage, and data-quality guarantees into the platform itself, so pipeline health, schema drift, and data freshness are monitored, first-class properties rather than tribal knowledge. • Partner with security and compliance stakeholders to design tenant-isolation and audit controls that hold up under the most stringent enterprise and government scrutiny. • Guide the long-term modernization of adjacent, high-cost data infrastructure as part of the same overall data strategy.