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Analytics Engineer, Infrastructure Strategy

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Salary

Compensation: $245K – $385K

As a critical member of the Applied Engineering Analytics Data team, you will be instrumental in enhancing our understanding of our infrastructure, particularly our GPU fleet: its allocation, its utilization, its costs, and opportunities for optimization. This role is pivotal to ensuring we optimize on infrastructure investments, which is vital for our AI research and deployment activities. You will work on projects that develop key data sources and dashboards to provide actionable insights into infrastructure performance and efficiency, helping us to optimize our computational resources.

In this role, you will:

  • Collaborate with the engineering and operations teams to identify high-impact analytics problems related to infrastructure utilization and develop data-driven solutions.
  • Foster a data-centric culture by defining, tracking, and operationalizing key infrastructure performance metrics.
  • Lead cross-functional projects involving data collection, cleaning, and analysis from various sources, managing the lifecycle of metrics from prototyping to production.
  • Develop and refine dashboards and reports that empower teams to independently extract and analyze infrastructure-related data.
  • Design and implement data pipelines to consolidate data from multiple sources, ensuring that the resulting datasets are reliable and scalable.

You might thrive in this role if you are/have:

  • Over 9 years of experience in relevant Data / Analytics Engineering roles.
  • Expertise in SQL and experience with ETL workflows, especially in extracting and analyzing large datasets.
  • Experience with batch data pipeline development, including tools like Spark or similar technologies.
  • Strong data modeling skills, capable of designing OLAP data models using transactional tables to meet analytical requirements.
  • Proficiency in quantitative programming languages, with a preference for Python, to create data models and tools for analytics.
  • Familiarity with business intelligence tools such as Mode, Tableau, or Looker for data visualization and self-service analytics.
  • A meticulous attention to detail and a strong commitment to data accuracy and integrity.
  • Proven ability to build effective partnerships across diverse teams.
  • Experience designing end-to-end data systems, from data ingestion through consumption.
  • A track record of delivering substantial business impact
  • (Optional) Experience in domains involving resource optimization or hardware analytics.

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