Senior Data Engineer – Platform

🔥 2 minutes ago

🏄 California, New York, +2 more states – Remote

🏄 California – Remote 🗽 New York – Remote 🤠 Texas – Remote ☕ Washington – Remote

💵 $140k - $270.3k / year

⏰ Full Time

🟠 Senior

🚰 Data Engineer

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👻 Ghost score 1%

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Own systems end to end from ambiguous customer and operational needs through architecture, implementation, deployment, observability, incident response, and ongoing support • Design and maintain batch and streaming ingestion, transformation, reconciliation, and serving paths for fleet, capacity, utilization, cost, scheduling, and operational telemetry • Build shared libraries, workflow and DAG abstractions, deployment tooling, data contracts, and paved-road platform patterns • Engineer reliable distributed workloads and diagnose correctness and performance issues across applications, SQL engines, Spark jobs, storage systems, networks, and cloud services • Build for retries, idempotency, backfills, schema evolution, and partial failure • Apply least privilege, service identities, secrets management, access controls, environment isolation, auditability, and safe operational practices • Establish automated tests, data-quality checks, lineage, freshness and completeness monitoring, actionable alerting, SLOs, and clear ownership • Make trusted data usable through well-modeled tables, APIs, automation, dashboards, and focused internal applications • Lead build reviews, communicate tradeoffs, mentor engineers, and improve architecture, testing, debugging, and operational practices

🎯 Requirements

• BS or MS in Computer Science, Engineering, or a related field, or equivalent experience • 5+ years of experience building and operating production software, data platforms, backend infrastructure, databases, or distributed systems • Production proficiency in Python or another backend or systems language, with ability and willingness to work primarily in Python and SQL • Hands-on experience in distributed data processing, database architecture and operation at scale, production ETL, change-data-capture, streaming/event-processing systems, backend or cloud-platform systems, or strong SQL and data-modeling • Ability to debug unfamiliar systems across multiple layers using logs, metrics, traces, query plans, profiles, and controlled experiments • Experience operating services or pipelines in a cloud or complex production environment, including testing, CI/CD, monitoring, alerting, rollback, and incident response • Working knowledge of secure platform development, identity and access management, least privilege, secret handling, trust boundaries, and safe multi-environment deployments • Ability to make architectural tradeoffs, own work through ambiguity, and communicate effectively with users, partner teams, and engineers from different fields • Track record of learning unfamiliar technologies and domains and turning that learning into maintainable systems and reusable team practices • Experience with AI agents and LLM-supported workflow automation • Preferred experience with Databricks, Apache Spark, PySpark, Spark SQL, Delta Lake, Unity Catalog, Kafka, change-data capture, event systems, Elasticsearch/OpenSearch, AWS, Azure, GCP, Kubernetes, Slurm, compute clusters, GPU infrastructure, fleet-scale telemetry, agentic systems, LLM-enabled workflow automation, harness engineering, or AI-agent evaluation and operational tooling

🏖️ Benefits

• Equity • Benefits