Senior Staff Software Engineer, Data Engineering, Cloud AI

MINIMUM QUALIFICATIONS:

* Bachelor's degree or equivalent practical experience.

* 8 years of experience programming in languages such as Python, C++, Java, or

Go.

* 5 years of experience with design and architecture; and testing/launching

software products.

* 3 years of experience with software design and architecture.

* Experience building, debugging, or supporting large-scale data pipelines and

data processing systems.

PREFERRED QUALIFICATIONS:

* Master’s degree or PhD in Engineering, Computer Science, or a related

technical field.

* 8 years of experience with data structures and algorithms.

* Experience leading engineering teams, mentoring talent, and architecting

solutions for complex data challenges.

* Experience driving cross-functional collaboration and delivering strategic

technical recommendations to executive leadership.

* Experience building large-scale analytics platforms and data warehousing

solutions using distributed pipeline frameworks.

ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change

how billions of users connect, explore, and interact with information and one

another. Our products need to handle information at massive scale, and extend

well beyond web search. We're looking for engineers who bring fresh ideas from

all areas, including information retrieval, distributed computing, large-scale

system design, networking and data storage, security, artificial intelligence,

natural language processing, UI design and mobile; the list goes on and is

growing every day. As a software engineer, you will work on a specific project

critical to Google’s needs with opportunities to switch teams and projects as

you and our fast-paced business grow and evolve. We need our engineers to be

versatile, display leadership qualities and be enthusiastic to take on new

problems across the full-stack as we continue to push technology forward.

In this role, you will

• join the Cloud AI Data Engineering team, and build the
• critical data infrastructure, log processing engines, and automated
• experimentation pipelines that power executive dashboards and guide product
• strategy.
• As a Data Engineer Tech Lead on this team, you will lead and design robust,
• compliant pipelines handling massive datasets and transitioning raw log signals
• into real-time metrics. You will directly influence how our customers measure
• their AI investment and how our engineering teams validate their feature
• rollouts.The Google Cloud AI Research team addresses AI challenges motivated by
• Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and
• many other industries. We work on a range of unique problems focused on research
• topics that maximize scientific and real-world impact, aiming to push the
• state-of-the-art in AI and share findings with the broader research community.
• We also collaborate with product teams to bring innovations to real-world impact
• that benefits our customers. Individual pay is determined by factors including
• job-related skills, experience, and relevant education or training.
• US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
• Learn more about benefits at Google
• [https://www.google.com/about/careers/applications/benefits/].
• RESPONSIBILITIES:
• * Define the technical roadmap and goal for the Gemini Enterprise metrics and
• analytics ecosystem, aligning with broader Cloud AI product goals.
• * Architect scalable batch and streaming pipelines, resolving complex analytics
• ambiguities and mitigating infrastructure roadblocks before they impact
• delivery.
• * Partner with cross-functional leaders in Data Science, Product Management,
• and Engineering to identify future metrics requirements and align project
• timelines.
• * Mentor and grow the team, providing guiding feedback and creating
• opportunities for engineers to stretch into challenging technical
• responsibilities.
• * Ensure system health and enforce Service Level Agreements/Service Level
• Objective (SLAs/SLOs) for critical production datasets, engineering best
• practices like robust testing, privacy compliance, and data governance.