About The Position

We are seeking a Technical Project Manager to lead analytics projects for Nvidia's on prem data centers. This role offers an outstanding opportunity to develop the future of data engineering at NVIDIA, a company known for its groundbreaking innovations and outstanding talent. You will translate business needs into clear technical requirements, lead all aspects of delivery of data pipelines and analytics products, and ensure high-quality insights across the full data center lifecycle: planning, equipment demand and procurement, asset management, installs, power utilization, break/fix, and decommissioning.

Requirements

  • More than 8 years of experience in a data center operations–related area (e.g., data center engineering, facilities/operations, planning, infrastructure operations).
  • BS, BA, or BEng degree in a technical field, or equivalent experience.
  • Demonstrated ability in providing analytics solutions (data products, dashboards, pipelines, reporting) in roles such as Data Engineer, Product Owner, Analytics PM, or equivalent.
  • Proficiency with SQL for data exploration and validation on large datasets, including time series data.
  • Experience implementing pipelines / ETL/ELT, including understanding dependencies, SLAs, and failure modes.
  • Experience handling data center infrastructure information: racks and devices, asset inventories, power consumption, equipment needs and orders, ASN/shipping details, and lifecycle events (install, break/fix, decommission).
  • Proven skill in gathering and documenting requirements, composing clear user stories and acceptance criteria, and working closely with engineers.
  • Superb communication skills; able to align technical teams and operations collaborators and bring decisions to completion.

Nice To Haves

  • Experience with building software prototypes using AI tools such as Cursor, Vibe or Lovable.
  • Experience with InfluxDB (or similar time series database), Databricks, Nautobot DCIM and SAP backend procurement data.

Responsibilities

  • Partner with collaborators to understand use cases, define scope, and capture detailed requirements.
  • Map business requirements to existing data structures, identify data gaps, and drive solutions with data and system owners.
  • Develop user requirements and success criteria; partner with engineers to segment work into tasks and handle backlogs.
  • Lead end-to-end delivery of analytics projects, including planning, prioritization, execution tracking, and communication with collaborators.
  • Support building and deployment of data pipelines and models, including large time series datasets (e.g., power/utilization, telemetry, capacity metrics).
  • Define baselines, validation criteria, and data quality checks to ensure trust in metrics, dashboards, and reports.
  • Help define and refine benchmarks and reporting that provide insight into data center capacity, utilization, health, and operational efficiency.

Benefits

  • equity
  • benefits
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