Nvidia Senior Data Engineer Telemetry Infrastructure 

Israel, North District 

407448655

In this role, you’ll design, build, and maintain scalable, high-performance data pipelines that handle massive volumes of real-time telemetry data from hardware, communication modules, firmware, and large-scale AI and HPC clusters.

What you’ll be doing:

• Define and execute the group’s data technical roadmap, aligning with R&D, hardware, and DevOps teams

• Design and maintain flexible ETL/ELT frameworks for ingesting, transforming, and classifying telemetry and performance data

• Build and optimize streaming pipelines using Apache Spark, Kafka, and Databricks, ensuring high throughput, reliability, and adaptability to evolving data schemas

• Implement and maintain observability and data quality standards, including schema validation, lineage tracking, and metadata management

• Develop monitoring and alerting for pipeline health using Prometheus, Grafana, or Datadog

• Support self-service analytics for engineers and researchers via Databricks notebooks, APIs, and curated datasets

• Promote best practices in data modeling, code quality, security, and operational excellence across the organization

• Deliver reliable insights for cluster performance analysis, telemetry visibility, and end-to-end test coverage

What we need to see:

• B.Sc. or M.Sc. in Computer Science, Computer Engineering, or a related field

• 5+ years of hands-on experience in data engineering or backend development

• Strong practical experience with Apache Spark (PySpark or Scala) and Databricks

• Expertise with Apache Kafka, including stream ingestion, schema registry, and event processing

• Proficiency in Python and SQL for data transformation, automation, and pipeline logic

• Familiarity with ETL orchestration tools (Airflow, Prefect, or Dagster)

• Experience with schema evolution, data versioning, and validation frameworks (Delta Lake, Iceberg, or Great Expectations)

• Solid understanding of cloud environments (AWS preferred; GCP or Azure also relevant)

• Knowledge of streaming and telemetry data architectures in large-scale, distributed systems

Ways to stand out from the crowd:

• Exposure to hardware, firmware, or embedded telemetry environments.

• Experience with real-time analytics frameworks (Spark Structured Streaming, Flink, Kafka Streams)

• Experience with data cataloging or governance tools (DataHub, Collibra, or Alation)

• Familiarity with CI/CD for data pipelines andinfrastructure-as-code(Terraform, GitHub Actions)

• Experience designing performance metrics data systems (latency, throughput, resource utilization) that support high-volume, high-frequency telemetry at scale