Senior Data Engineer

NVIDIA NVIDIA · Semiconductors · Raanana, Israel +1

Senior Data Engineer at NVIDIA to lead data-driven decision-making and shape next-gen Data Center monitoring, analytics, and management platforms. Owns large-scale telemetry and analytics pipelines end-to-end, focusing on data quality, scalability, and architectural excellence. Partners with product and infrastructure teams, and co-designs analytics and ML-ready architectures with data scientists.

What you'd actually do

  1. Act as the technical owner and primary point of contact for ML/AI data pipelines, large‑scale data ingestion, and enterprise data warehousing.
  2. Lead the design and onboarding of new, high‑volume data sources to continuously expand and mature the centralized Data Lake.
  3. Partner with multiple NVIDIA product and infrastructure teams to translate complex business and technical requirements into scalable data solutions.
  4. Work closely with system architects, platform engineers, and data scientists to co‑design end‑to‑end analytics and ML‑ready architectures.
  5. Design, build, and optimize high‑performance ETL pipelines with strong guarantees around data integrity, reliability, and observability.

Skills

Required

  • BSc. or MSc. in Computer Science, Computer Engineering, or a related field.
  • 5+ years of hands‑on experience in Data Engineering, large‑scale telemetry systems, and big‑data platforms.
  • Deep understanding of telemetry architectures, automation technologies, and modern application platforms and paradigms.
  • Strong familiarity with networking fundamentals.
  • Proven experience with modern analytics and big‑data platforms such as Spark, Databricks, and similar ecosystems.
  • Solid understanding of different telemetry pipeline models and trade‑offs (streaming vs. batch, push vs. pull, real‑time vs. near‑real‑time).
  • Advanced programming skills and a track record of building production‑grade data pipelines, tooling, and proof‑of‑concepts.

Nice to have

  • Experience in data‑center environments is a strong advantage.
  • Demonstrated ability to prototype complex ideas quickly and clearly articulate their business and technical value.
  • Experience with cloud‑native development, deployment, and operational best practices.
  • Hands‑on experience with public cloud platforms (AWS, GCP, Azure).
  • Background in large‑scale data center architecture and infrastructure technologies.
  • Contributions to open‑source projects or active participation in the data engineering community.

What the JD emphasized

  • ML/AI data pipelines
  • large-scale telemetry systems
  • big-data platforms
  • telemetry architectures
  • data-center environments
  • analytics and big-data platforms
  • telemetry pipeline models
  • production-grade data pipelines

Other signals

  • ML/AI data pipelines
  • large-scale data ingestion
  • enterprise data warehousing
  • centralized Data Lake
  • scalable data solutions
  • ML-ready architectures
  • high-performance ETL pipelines
  • data integrity, reliability, and observability
  • monitoring, telemetry, and analytics platforms