Senior Data and Platform Engineer

NVIDIA is seeking a Senior Data Platform Engineer to build and operate shared data infrastructure for its DGX Cloud organization. The role leads architecture and delivery of batch and streaming data systems, develops platform capabilities and trusted data products, and provides technical leadership for production investigations, engineering standards, and cross-team initiatives.

Responsibilities:

• Define and guide the technical vision for a key DGX Cloud Data Platform domain covering various services, pipelines, data products, and consumer teams. Take responsibility for its architecture, interfaces, and growth, while foreseeing needs related to scale, reliability, performance, security, compatibility, and cost
• Lead the technical delivery of complex, cross-team initiatives. Transform unclear requirements into well-defined architectures and interfaces, coordinate implementation methods among contributors, personally write essential code, overcome technical obstacles, and guide integrations securely into production
• Architect, implement, and evolve batch and streaming systems that ingest, transform, reconcile, and serve fleet, capacity, utilization, cost, scheduling, and operational telemetry at scale across multiple environments and consumers
• Build shared platform capabilities—including libraries, workflow and orchestration abstractions, deployment tooling, and implementation standards—that are adopted across teams and measurably improve delivery speed, reliability, operational effort, and cost
• Serve as the technical lead for high-impact production investigations spanning pipelines, applications, query engines, distributed processing, storage, networks, and cloud services. Coordinate across owners, establish root cause, drive durable resolution, and ensure preventive improvements are implemented
• Establish and drive adoption of engineering standards for automated testing, data quality, reconciliation, lineage, service-level objectives, observability, secure service identities, least-privilege access, release readiness, and auditable deployments
• Establish robust data models, semantics, ownership boundaries, and serving interfaces across teams. Provide tables, APIs, automation, dashboards, and internal applications that ensure trusted DGX Cloud data is widely accessible without sacrificing accuracy or maintainability
• Provide technical leadership through architecture and build reviews, hands-on mentorship of senior engineers, and evidence-based resolution of difficult tradeoffs. Raise engineering quality across teams through reusable patterns, clear decisions, and sustained follow-through

Requirements:

• 8+ years of relevant industry experience with a Bachelor's or equivalent experience, and a Master's degree or equivalent experience in Computer Science, Engineering, or a related field
• A sustained record of personally crafting, implementing, and operating production software, data platforms, databases, or distributed systems
• This includes end-to-end technical ownership of a multi-system platform domain or a complex cross-team engineering initiative
• Experience includes deep hands-on work with distributed processing, analytical or relational databases, production ETL, change-data capture, streaming or event processing, or backend and cloud systems handling large data volumes
• Strong software engineering fundamentals and production proficiency in a backend or systems language, with deep experience using data-processing and platform libraries or frameworks to build reliable systems
• Experience crafting reusable abstractions, reviewing substantial changes, and debugging critical code paths
• Equivalent depth across different technology stacks is welcome
• Strong SQL and data-modeling skills, including practical depth in query execution, incremental processing, schema evolution, consistency, analytical consumption, idempotency, replay, late-arriving data, partial failure, and cross-system correctness
• Demonstrated skill in diagnosing failures across various systems by analyzing logs, metrics, traces, query plans, profiles, and controlled experiments, followed by applying and confirming long-lasting solutions
• Strong architectural judgment in assessing tradeoffs among reliability, performance, cost, security, compatibility, and maintainability, including experience guiding major migrations or architectural changes across teams without interrupting production service
• Experience establishing production safeguards and engineering practices that multiple teams adopt, including automated testing, CI/CD, monitoring, alerting, rollback, incident response, and secure deployment
• Deep experience with distributed data processing and lakehouse architectures, including optimization, reliability, and operation at production scale
• Equivalent experience with large-scale database or data-processing platforms is welcome
• Experience crafting and operating distributed streaming or event-driven systems, including partitioning, consumer behavior, flow control, replay, delivery guarantees, and schema evolution
• Experience leading the scaling, migration, or performance improvement of relational, distributed, time-series, object-storage, or data systems specialized in managing searchable content
• Background operating cloud infrastructure, container orchestration, workload schedulers, compute or GPU clusters, and fleet-scale telemetry
• Experience defining and owning the production adoption of agentic systems or workflow automation
• You should focus on evaluation, permissions, observability, failure recovery, and measurable improvements in engineering efficiency or operational outcomes