About this role
NVIDIA is hiring a Senior Data Engineer to lead and evolve large-scale telemetry and analytics pipelines for next-generation data center monitoring, analytics, and management. You will own end-to-end ML/AI data pipeline architecture, expand the centralized Data Lake, and build high-performance ETL pipelines with strong guarantees around data integrity and observability.
Key Responsibilities
- Act as technical owner for ML/AI data pipelines and enterprise data warehousing
- Design and onboard new high-volume data sources into the centralized Data Lake
- Build and optimize high-performance ETL pipelines with data integrity, reliability, and observability
- Collaborate with system architects, platform engineers, and data scientists on analytics and ML-ready architectures
- Provide technical leadership and mentorship for data engineering and data-driven decisionmaking
Technical Overview
This role focuses on designing and operating large-scale telemetry and analytics data pipelines, including enterprise data warehousing, data ingestion, and production-grade ETL. Core stack includes Spark and Databricks, with emphasis on streaming vs. batch and push vs. pull telemetry pipeline models and cloud-native development on AWS/Azure/GCP.
Ideal Candidate
The ideal candidate is a senior data engineer with 8+ years building large-scale telemetry and analytics pipelines, including production-grade ETL. They have deep experience with telemetry pipeline tradeoffs (streaming vs. batch, push vs. pull, realtime vs. nearrealtime) and strong experience with Spark and Databricks, plus hands-on cloud experience (AWS, GCP, Azure). They can act as technical owner for ML/AI data pipelines and provide leadership through mentorship and cross-team collaboration.
Must-Have Skills
8+ years of handson experience in Data Engineeringlarge-scale telemetry systemsbigdata platformsAdvanced programming skillsbuilding productiongrade data pipelinesProven experience with modern analytics and bigdata platforms such as SparkDatabricksand similar ecosystemsSolid understanding of different telemetry pipeline models and tradeoffs (streaming vs. batchpush vs. pullrealtime vs. nearrealtime)BSc or equivalent experience or MSc. in Computer ScienceComputer Engineeringor a related fieldETL pipelinesdata integrityreliabilityobservability
Nice-to-Have Skills
cloudnative developmentdeploymentand guidelinesHandson experience with public cloud platforms (AWSGCPAzure)Background in largescale data center architecture and infrastructure technologiesContributions to opensource projects or active participation in the data engineering communityDemonstrated ability to prototype complex ideas quickly and clearly articulate their business and technical value
Tools & Platforms
SparkDatabricksAWSAmazon Web ServicesGCPGoogle Cloud PlatformAzure
Required Skills
Data EngineeringML/AI data pipelinestelemetry architecturesETL pipelinesdata integrityreliabilityobservabilitySparkDatabricksstreaming vs. batchpush vs. pullrealtime vs. nearrealtimeAWSGCPAzureData Lakeenterprise data warehousing
Hard Skills
data engineeringlarge-scale telemetry systemsbigdata platformstelemetry architecturesautomation technologiesmodern application platforms and paradigmsnetworking fundamentalsETL pipelinesdata integritydata reliabilityobservabilityML/AI data pipelineslarge-scale data ingestionenterprise data warehousingdata qualityscalabilityarchitectural excellencetechnical leadershipSparkDatabricksstreamingbatchpushpullrealtimenearrealtimepublic cloud platformsAWSAmazon Web ServicesGCPGoogle Cloud PlatformAzurecloud-native developmentcodeesign end-toend analytics and MLready architecturesData Laketelemetry pipeline modelsdata ingestion
Soft Skills
leadershipmentorshipguiding teamsprototype complex ideas quicklyarticulate business and technical valuecross-functional collaborationtechnical mentorshipcommunicationpartnering with product and infrastructure teamsworking closely with system architectsplatform engineersand data scientists
Keywords for Your Resume
Senior Data EngineerNetworking ArchitectureData EngineeringML/AI data pipelineslarge-scale data ingestionenterprise data warehousingData LakeETL pipelinesdata integrityreliabilityobservabilitytelemetry architecturestelemetry pipeline modelsstreaming vs. batchpush vs. pullrealtime vs. nearrealtimeSparkDatabrickstelemetry systemsdata qualitycloud-native developmentAWSAmazon Web ServicesGCPGoogle Cloud PlatformAzuredata center environments
Deal Breakers
8+ years of hands-on Data Engineering experience with large-scale telemetry systems, Proven experience with Spark and Databricks, Strong understanding of telemetry pipeline tradeoffs (streaming vs. batch, push vs. pull, realtime vs. nearrealtime), BSc or equivalent or MSc in Computer Science/Computer Engineering (or related field)
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