AI Data Engineer I, Professional Services, Google Cloud

Google Cloud Consulting Professional Services helps customers design and deliver data and AI solutions on Google Cloud Platform (GCP). As an AI Data Engineer I in Seattle, you’ll guide customers through ingesting, storing, processing, analyzing, exploring, and visualizing data on GCP, while partnering with teams to implement Google Cloud products. The role combines hands-on technical work, best-practice guidance, and customer-facing collaboration in a consulting environment.

Location: Seattle, WA (onsite)
Compensation: USD 130,000 - 187,000 per year

What you’ll do

• Serve as a trusted technical advisor, solving complex Big Data and AI challenges for customers.
• Develop and deliver best practice recommendations, including tutorials, blog articles, sample code, and technical presentations tailored to business and technical stakeholders.
• Evaluate on-premises and cloud database environments and consult on optimal design for performance and deployment on GCP.
• Travel regularly, up to 30%, for in-region meetings, technical reviews, and onsite delivery activities.
• Use effective communication via video conferencing for meetings, technical reviews, and onsite delivery coordination.
• Collaborate with customers and internal teams to implement Google Cloud products, including data migrations and transformations, data pipeline building, and advisory on Big Data/AI challenges.

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, and writing software in Java, Python, or SQL.

Technologies

• Spark, Beam, Java, Python, SQL, and Google Cloud Platform (GCP)

Preferred qualifications

• 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 and developing production-grade Big Data solutions in virtualized environments

Benefits

• Insurance: health, dental, vision, life, and disability
• Retirement: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at 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
• Leaves: Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks; Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year