Distinguished Software Engineer, Data Platform
Job description
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.
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.
Technical Leadership & Organizational Influence
• Serve as the most senior technical authority on data architecture across the engineering
organization, setting direction that spans multiple teams and multi-year roadmaps.
• Translate deep technical tradeoffs into clear, executive-ready recommendations, and build
lasting organizational consensus around them.
• Mentor senior and principal engineers across the organization and raise the overall bar for
data architecture practice.
• Deep, hands-on expertise with distributed database engines that support mixed transactional and analytical workloads at scale — internal architecture, benchmarking methodology, and real production trade-offs, not just theoretical familiarity.
• Proven experience architecting real-time data-replication and streaming pipelines at scale, including schema evolution, delivery-guarantee tradeoffs, and multi-consumer fan-out.
• Strong background in multi-tenant SaaS data architecture: tenant isolation models, per-tenant routing, and governed, self-service data access patterns.
• Experience modernizing large-scale data infrastructure — search, logging, or observability — including cost optimization at scale.
• Track record of running rigorous, benchmark-backed build-vs-buy evaluations and defending the resulting recommendations to executive and security stakeholders.
• Comfortable operating across multiple cloud providers and translating platform decisions into cost and compliance outcomes.