Technical Program Manager II, Data Analytics, Global Network Operations

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 2 years of experience in program management.
  
• Experience managing programs involving reporting, dashboards, or data visualization.
• Experience working with datasets with SQL and Python.
  

Preferred qualifications:

• 2 years of experience managing cross-functional or cross-team projects.
  
• Experience managing programs related to worldwide optical and IP network operations.
• Knowledge of networks and associated metrics related to traffic and capacity.
• Passion for trustworthy data, transparent metrics and making technical language accessible to the wider organization.
  

About the job

A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.

Our goal is to build a Google that looks like the world around us — and we want Googlers to stay and grow when they join us. As part of our efforts to build a Google for everyone, we build diversity, equity, and inclusion into our work and we aim to cultivate a sense of belonging throughout the company.

From telemetry and topology, to risk and operations, Global Network Operations (GNO) relies on multiple datasets. As a Technical Program Manager for Data Analytics, you'll help ensure all operational network teams have access to reliable, trustworthy, and transparent data for day to day operations.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

The US base salary range for this full-time position is $122,000-$178,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Collaborate with team members and stakeholders to understand or identify defined work problems and program goals, obtain prioritized deliverables, and discuss program impact.
  
• Leverage domain knowledge in network operations to prioritize program goals, understand and translate other stakeholders’ needs, identify opportunities for improvement, and implement metrics alignment.
  
• Define the scope of projects and develop, execute, or manage project plans to increase operational efficiency and achieve sublinear network growth.
  
• Develop accurate and efficient queries and lead the production of reliable metrics to monitor network performance, growth, repairs, and issues.
  
• Support internal teams in GNO with data science programs from ideation to data collection, hypothesis testing, and evaluation, and ensure reliable data pipelines run smoothly.