Staff Software Engineer, Gemini App, Data Engineering, Deepmind

Google Google · Big Tech · Mountain View, CA +3

Staff Software Engineer on the Gemini Apps Data Engineering team at Google DeepMind. This role focuses on architecting and operating data pipelines for Gemini, a large-scale AI product. Responsibilities include building scalable data pipelines for analytics and ML/AI training, ensuring data quality and reliability, and providing technical leadership to a team of engineers. The role involves working with stakeholders across Data Science, model release, and DeepMind to solve data challenges and support Gemini's growth.

What you'd actually do

  1. Work closely with our Data Science counterparts in definition and driving the goals for a Modern Data Warehouse.
  2. Architect and build scalable batch and real-time pipelines that power experimentation, product analytics, and ML/AI training loops.
  3. Own data quality, reliability, and observability end-to-end, including defining and operating Service Level Agreements (SLAs)/ Service Level Objectives (SLOs) for critical production datasets.
  4. Mentor and grow other engineers, raising the technical bar through design reviews, code reviews, and actionable feedback.
  5. Drive cross-functional engineering initiatives that span multiple teams, translating ambiguous requirements into technical designs.

Skills

Required

  • Bachelor's degree in Computer Science or related technical field, or equivalent practical experience.
  • 8 years of experience in software development.

Nice to have

  • Experience with Apache Spark, Flink, Beam, Airflow, or BigQuery/Snowflake or other similar infrastructure.
  • Experience developing, debugging, and supporting large-scale data pipelines.
  • Experience with distributed data processing frameworks and workflow orchestration tools.
  • Experience in Go/C++.
  • Experience in a technical Lead or similar role, ideally setting technical direction for a small group of executives and software engineers.
  • Ability to address daily business data requirements, ensure that development obstacles are cleared to maintain the Gemini App's changing growth.

What the JD emphasized

  • massive global scale
  • technical leadership necessary to scale our infrastructure
  • scale the data pipelines that drive Gemini Apps’s analytics and power model and product improvement
  • critical telemetry across all Gemini surfaces
  • massive growth

Other signals

  • architect and build scalable batch and real-time pipelines
  • power experimentation, product analytics, and ML/AI training loops
  • massive global scale
  • technical leadership necessary to scale our infrastructure