AI Data Engineer I, Professional Services, Google Cloud

Google•Boulder, CO

3h•$130,000 - $187,000

About The Position

The Google Cloud Consulting Professional Services team guides customers through important stages of their cloud journey, helping businesses transform using Google’s global network, web-scale data centers, and software infrastructure. As part of this team, the AI Data Engineer helps customers ingest, store, process, analyze, explore, and visualize data on Google Cloud Platform. The role leads data migrations and transformations, partners with clients to architect scalable data processing systems, builds efficient data pipelines, resolves platform challenges, and collaborates with strategic cloud customers and Google teams to implement Google Cloud products successfully. The position also serves as a trusted technical advisor, addressing complex Big Data and AI challenges and recommending optimal designs for performance and deployment on Google Cloud Platform. It includes creating technical guidance and materials for stakeholders with varying levels of business and technical expertise. Regular in-region travel of up to 30% is required for meetings, technical reviews, and onsite delivery activities. Individual pay is based on job-related skills, experience, and relevant education or training. The listed US compensation is $130,000–$187,000, plus a 15% bonus target, equity, and benefits.

Requirements

Bachelor's degree in Computer Science, a related field, or equivalent practical experience.
5 years of experience with data processing software (e.g., Spark, Beam) and algorithms.
Experience managing client-facing projects, troubleshooting technical issues, and working with Engineering and Sales Services teams.
Experience with database administration techniques or data engineering, as well as writing software in Java, Python, or SQL.

Nice To Haves

Google Cloud Platform (GCP) Professional Data Engineer Certification.
Experience building machine learning solutions and working with technical customers.
Experience in technical consulting and working with data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments.
Experience building production LLM/GenAI systems, including multi-agent orchestration, agent frameworks (e.g., ADK), tool and function calling, RAG, and prompt/context development.
Experience architecting, developing software, or building internet-scale, production-grade Big Data solutions in virtualized environments.

Responsibilities

Act as a trusted technical advisor to customers and solve complex Big Data and AI challenges.
Create and deliver best-practice recommendations, tutorials, blog articles, sample code, and technical presentations, tailoring the approach and messaging to stakeholders with varied levels of business and technical expertise.
Analyze on-premises and cloud database environments and consult on optimal design for performance and deployment on Google Cloud Platform.
Travel regularly, up to 30% of the time, in-region for meetings, technical reviews, and onsite delivery activities.
Communicate effectively via video conferencing for meetings, technical reviews, and onsite delivery activities.
Guide customers on ingesting, storing, processing, analyzing, exploring, and visualizing data on Google Cloud Platform.
Lead data migrations and transformations, partner with clients to architect scalable data processing systems, build efficient data pipelines, and resolve platform challenges.
Collaborate with strategic cloud customers and Google teams to implement Google Cloud products successfully.