Data and Platform Engineer

NVIDIA

. Own a major Navigator data platform component and its roadmap, including architecture, interfaces, technical goals, and evolution .

Posted 9/22/2026full-timeRemote • United StatesSeniorLead💰 $200,000 - $322,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in building and operating data platforms, with strong proficiency in Python and SQL. Capable of leading complex technical projects, establishing data models, and implementing robust testing and deployment practices.

Highest-signal resume keywords

Data Platform OwnershipDistributed Processing with SparkSQL and Data ModelingTechnical Project LeadershipCI/CD and Secure Deployment Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLApache SparkDatabricksETLData ModelingChange-Data CaptureStreaming SystemsProduction Software DevelopmentArchitectural Judgment

Soft Skills

CommunicationMentoringProblem-SolvingInfluencing DecisionsSimplifying Complex Issues

Tools & Technologies

KubernetesElasticsearchOpenSearchAWSAzureGCPKafkaDelta LakeUnity CatalogSlurm

Industry Keywords

Data QualityOperational TelemetryIncident ResponseMonitoringAlerting

Tech Stack

Tools & technologies

ApacheAWSAzureCloudDistributed SystemsElasticSearchETLGoogle Cloud PlatformKafkaKubernetesPySparkPythonSparkSQLUnity

About the role

Key responsibilities & impact

Own a major Navigator data platform component and its roadmap, including architecture, interfaces, technical goals, and evolution
Anticipate capacity, compatibility, and operational needs over a multi-year horizon
Lead technical delivery across teams by clarifying requirements, breaking down work, managing dependencies and risks, and keeping stakeholders aligned
Build batch and streaming ingestion, transformation, reconciliation, and serving processes for fleet, capacity, utilization, cost, scheduling, and operational telemetry
Establish stable data models and agreements as sources, consumers, and scale evolve
Direct creation and adoption of libraries, workflow/DAG abstractions, deployment tools, and standard implementation approaches
Lead complex production investigations across pipelines, applications, SQL engines, Spark, storage, networks, and cloud services
Define and implement testing, data-quality, reconciliation, lineage, SLO, and release-readiness standards
Partner with security and infrastructure teams on trust boundaries, service identities, least privilege, secrets, environment isolation, and auditability
Deliver well-modeled tables, APIs, automation, dashboards, and focused internal applications
Guide design reviews, mentor engineers taking on larger ownership, and resolve technical disagreements
Partner with leadership on priorities and explain how technical investments support DGXC objectives

Requirements

What you’ll need

BS or MS in Computer Science, Engineering, or a related field (or equivalent experience)
At least 12+ years of equivalent experience
Sustained record of building and operating production software, data platforms, databases, or distributed systems
Experience owning a major component or complex project from requirements and architecture through release and ongoing operation
Ability to outline technical plans, establish objectives, assign design and implementation tasks, and guide delivery with little supervision
Practical experience with distributed processing using Spark or similar systems, databases, production ETL, change-data capture, streaming, event handling, or backend and cloud platforms managing large data volumes
Strong software-engineering fundamentals and production proficiency in Python or another backend or systems language
Ability and willingness to work primarily in Python and SQL
Experience designing reusable abstractions, reviewing substantial changes, and implementing and debugging critical code paths
Strong SQL and data-modeling skills, including query execution, incremental processing, schema evolution, consistency, and analytical consumption
Ability to reason about idempotency, replay, late-arriving data, partial failure, and correctness across system boundaries
Experience leading complex investigations involving multiple components and teams
Ability to use logs, metrics, traces, query plans, profiles, and controlled experiments to establish root cause and prevent recurrence
Demonstrated architectural judgment balancing reliability, performance, cost, security, compatibility, and maintainability
Experience leading significant migrations or architectural changes while preserving production service
Experience establishing testing, CI/CD, monitoring, alerting, rollback, incident response, and secure deployment practices
Proven success influencing technical decisions without official authority and advancing workflow improvements across related teams
Ability to simplify complex issues and communicate decisions and delivery risks clearly
Databricks, Apache Spark, PySpark, Spark SQL, Delta Lake, or Unity Catalog expertise is advantageous
Kafka or comparable streaming systems experience is advantageous
Experience with Elasticsearch or OpenSearch is advantageous
Experience with Kubernetes, Slurm, cloud infrastructure, and fleet telemetry across AWS, Azure, GCP, or other providers is advantageous
Experience building production agentic systems or agent harnesses is advantageous

Benefits

Comp & perks

Equity
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