Hybrid Tech Lead Manager - Data Query & Storage

Waymo; California (MO); Hybrid; USD 251,000 - 310,000; Full time

Benefits offered by this job

Annual bonus

Equity incentive

Benefits package

Job summary

Waymo is seeking an experienced software engineering leader to scale the Waymo Driver data platform. You will lead and grow a team of engineers, defining the roadmap for data pipeline, storage, and query infrastructure across driver evolution and commercialization.

You will design and operate massively scalable distributed systems for data processing, collaborating with engineering and data science teams, and own the full lifecycle from architecture to monitoring.

Qualifications

• BS/MS/PhD in Computer Science or related field.
• 8+ years of software engineering experience with distributed systems.
• Deep understanding of query engines (F1 Query, Presto) and storage (Bigtable, Spanner).
• 2+ years in engineering management or tech leadership.
• Strong programming in SQL and C++/Java/Python.
• Excellent problem-solving and communication skills.

Responsibilities

• Lead, mentor, and grow a team of software engineers.
• Define and execute the data pipeline, storage, and query roadmap.
• Drive design, development, and operation of scalable distributed systems.
• Collaborate with Engineering and Data Science to deliver impactful solutions.
• Own end-to-end lifecycle from architecture to monitoring.
• Champion best practices in software development and operations.

Skills

Team leadership

Distributed systems

SQL

C++/Java/Python

Problem solving

Education

BS, MS, or PhD in Computer Science or related field

Tools

F1 Query

Presto

Bigtable

Spanner

Spark

Flume

Job description

Waymo is seeking an experienced software engineering leader to scale the Waymo Driver data platform. You will lead and grow a team of engineers, defining the roadmap for data pipeline, storage, and query infrastructure across driver evolution and commercialization.

You will design and operate massively scalable distributed systems for data processing, collaborating with engineering and data science teams, and own the full lifecycle from architecture to monitoring.