Senior Data and Platform Engineer

NVIDIA

. Define and guide the technical vision for a key DGX Cloud Data Platform domain .

Posted 10/5/2026full-timeRemote • United StatesSenior💰 $200,000 - $322,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in architecting and implementing scalable data platforms, with a strong focus on distributed processing, cloud systems, and automated testing practices. Proven ability to lead cross-team initiatives and establish engineering standards for high-quality production software.

Highest-signal resume keywords

Distributed Processing ExpertiseCloud Infrastructure ManagementSQL and Data-Modeling SkillsAutomated Testing and CI/CD PracticesArchitectural Judgment in Performance and Security

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Backend Programming LanguagesData PlatformsDistributed SystemsETL and Change-Data CaptureStreaming and Event ProcessingData-Processing LibrariesQuery ExecutionSchema EvolutionDebugging Critical Code PathsData Quality and Reconciliation

Soft Skills

Technical LeadershipMentorshipProblem-SolvingCollaborationDecision-Making

Tools & Technologies

Cloud ServicesContainer OrchestrationWorkload SchedulersTelemetry ToolsMonitoring and Alerting Systems

Industry Keywords

Data QualityObservabilityService-Level ObjectivesProduction SoftwareLakehouse Architectures

Tech Stack

Tools & technologies

CloudDistributed SystemsETLSQL

About the role

Key responsibilities & impact

Define and guide the technical vision for a key DGX Cloud Data Platform domain
Own architecture, interfaces, and growth while planning for scale, reliability, performance, security, compatibility, and cost
Lead technical delivery of complex, cross-team initiatives
Transform unclear requirements into architectures and interfaces, coordinate implementation, write essential code, overcome technical obstacles, and guide secure production integrations
Architect, implement, and evolve batch and streaming systems for fleet, capacity, utilization, cost, scheduling, and operational telemetry
Build shared platform capabilities, including libraries, workflow and orchestration abstractions, deployment tooling, and implementation standards
Lead high-impact production investigations across pipelines, applications, query engines, distributed processing, storage, networks, and cloud services
Establish engineering standards for automated testing, data quality, reconciliation, lineage, service-level objectives, observability, secure identities, least-privilege access, release readiness, and auditable deployments
Establish data models, semantics, ownership boundaries, and serving interfaces across teams
Provide tables, APIs, automation, dashboards, and internal applications for trusted DGX Cloud data access
Provide technical leadership through architecture and build reviews, hands-on mentorship, and resolution of difficult tradeoffs
Raise engineering quality through reusable patterns, clear decisions, and sustained follow-through

Requirements

What you’ll need

12+ years of relevant industry experience
Bachelor’s degree or equivalent experience
Master’s degree or equivalent experience in Computer Science, Engineering, or a related field
Sustained hands-on experience crafting, implementing, and operating production software, data platforms, databases, or distributed systems
End-to-end technical ownership of a multi-system platform domain or complex cross-team engineering initiative
Deep hands-on experience 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
Deep experience using data-processing and platform libraries or frameworks
Experience crafting reusable abstractions, reviewing substantial changes, and debugging critical code paths
Strong SQL and data-modeling skills
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 diagnosing failures using logs, metrics, traces, query plans, profiles, and controlled experiments
Strong architectural judgment across reliability, performance, cost, security, compatibility, and maintainability
Experience guiding major migrations or architectural changes across teams without interrupting production service
Experience establishing automated testing, CI/CD, monitoring, alerting, rollback, incident response, and secure deployment practices
Deep experience with distributed data processing and lakehouse architectures
Experience operating distributed streaming or event-driven systems
Experience leading scaling, migration, or performance improvement of relational, distributed, time-series, object-storage, or searchable-content data systems
Background operating cloud infrastructure, container orchestration, workload schedulers, compute or GPU clusters, and fleet-scale telemetry
Experience defining and owning production adoption of agentic systems or workflow automation

Benefits

Comp & perks

Equity
Comprehensive benefits package
Competitive salary