Software Engineer, Core Data Processing Infrastructure, Conduit

Minimum qualifications:

• Bachelor's in Computer Science, or related technical field, or equivalent practical experience.
• 2 years of experience in coding with C++.
• 2 years of experience with large-scale data processing.
• 2 years of experience in working with streaming.

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical field.
  
• Experience in coding with Java, Python or Go.
• Experience in architecting and developing large-scale distributed systems.

About the job

Conduit is the foundation of Core Data Processing products, encompassing capabilities from batch, incremental, streaming, to real-time data processing. It has successfully replaced a multitude of fragmented and costly legacy data infrastructures and continues to grow in adoption. The team's innovative work was recognized with Google Tech Impact Award in 2022, a testament to Conduit's significant contributions to the company.

In this role, you are committed to optimizing user experience around onboarding and pipeline management.

The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.
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

• Build highly scalable distributed infrastructure for batch and incremental data processing.
  
• Leverage AI to improve the velocity, efficiency and reliability. Build infrastructures for powering Generative AI products.
• Index, monitor and cache petabytes of data efficiently.
  
• Build AI agents to simplify the data engineering end-to-end journeys.
  
• Lead joint projects with key clients across Google.