Senior Data Engineer – Platform

NVIDIA

. Own systems end to end from ambiguous customer and operational needs through architecture, implementation, deployment, observability, incident response, and ongoing support .

Posted 9/21/2026full-timeRemote • United StatesSenior💰 $140,000 - $270,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating production software and data platforms, with a strong focus on Python, SQL, and distributed systems. Capable of leading architectural decisions, ensuring secure platform development, and implementing effective monitoring and incident response strategies.

Highest-signal resume keywords

Production Proficiency In PythonDistributed Data ProcessingDatabase Architecture And Operation At ScaleCloud Platform Systems ExperienceSecure Platform Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLDistributed SystemsETLChange-Data-CaptureStreaming SystemsData ModelingDebuggingIncident ResponseAI Agents

Soft Skills

Effective CommunicationMentoringProblem SolvingAdaptabilityCollaboration

Tools & Technologies

DatabricksApache SparkKafkaAWSAzureGCPKubernetesElasticsearchDelta LakeSlurm

Industry Keywords

Cloud ServicesData PlatformsOperational TelemetryProduction SoftwareFleet-Scale Telemetry

Tech Stack

Tools & technologies

ApacheAWSAzureCloudDistributed SystemsElasticSearchETLGoogle Cloud PlatformKafkaKubernetesPySparkPythonSparkSQLUnity

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

Equity
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