Staff Data Engineer: Scalable Analytics & ML Pipelines

Google DeepMind; New York (NY); On-site; USD 207,000 - 301,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

401(k) with company match

Paid time off

Job summary

Google DeepMind's Gemini Apps Data Engineering team architects and operates the data pipelines that deliver critical telemetry across Gemini surfaces. You will lead a small team of executive and junior engineers to scale data pipelines and drive key initiatives for fast, reliable analytics and model training data at a massive global scale.

You will collaborate with Data Science, model release, and feature teams, as well as across DeepMind, to solve problems and enable Gemini the next

Qualifications

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

Responsibilities

• Define and drive goals for a Modern Data Warehouse with Data Science counterparts.
• Architect and build scalable batch and real-time pipelines powering experimentation and ML/AI training loops.
• Own data quality, reliability, and observability end-to-end, including SLAs/SLOs for production datasets.
• Mentor and grow engineers through code reviews and feedback.
• Drive cross-functional initiatives spanning multiple teams and translate requirements into designs.

Skills

Go

C++

Data pipelines

Leadership

Go/CPP

Education

Bachelor's degree in Computer Science or related field

Tools

Apache Spark

Flink

Beam

Airflow

BigQuery/Snowflake

Job description

Google DeepMind's Gemini Apps Data Engineering team architects and operates the data pipelines that deliver critical telemetry across Gemini surfaces. You will lead a small team of executive and junior engineers to scale data pipelines and drive key initiatives for fast, reliable analytics and model training data at a massive global scale.

You will collaborate with Data Science, model release, and feature teams, as well as across DeepMind, to solve problems and enable Gemini the next