info_outline XIn accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: Health, dental, vision, life, disability insurance Retirement Benefits: 401(k) with company match Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA; Seattle, WA, USA. Minimum qualifications: Bachelor's degree in Computer Science or related technical field, or equivalent practical experience. 8 years of experience in software development. Preferred qualifications: 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 executive and junior software engineer. Ability to address daily business data requirements, ensure that development obstacles are cleared to maintain the Gemini App's changing growth. About the job The Gemini Apps Data Engineering team architects and operates the data pipelines that deliver critical telemetry across all Gemini surfaces. We combine deep technical expertise with Google-scale operating experience to solve the unique data issues inherent in building a transformative, global product during a time of massive growth!Gemini’s user base is growing continuously and our traffic has sometimes even doubled just over the course of a week. In this role, you will provide the technical leadership necessary to scale our infrastructure. You will architect durable data products and drive critical engineering initiatives across multiple teams to ensure we continue delivering metrics and training data quickly, reliably, and accurately at a massive global scale. Leveraging your experience in building and running scalable systems, and utilizing modern data warehouse infrastructure at Google, you will lead a small team of executive and junior engineers to scale the data pipelines that drive Gemini Apps’s analytics and power model and product improvement. You will work directly with key stakeholders, including our Data Science, model release, model quality, and feature teams, as well as teams across DeepMind, to identify and solve key problems to help make Gemini the next billion-user product. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Work closely with our Data Science counterparts in definition and driving the goals for a Modern Data Warehouse. Architect and build scalable batch and real-time pipelines that power experimentation, product analytics, and ML/AI training loops. 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. Mentor and grow other engineers, raising the technical bar through design reviews, code reviews, and actionable feedback. Drive cross-functional engineering initiatives that span multiple teams, translating ambiguous requirements into technical designs.