AI Data Engineer I, Professional Services, Google Cloud

Google Cloud Consulting Professional Services is seeking an AI Data Engineer I to support customers in designing end-to-end data solutions on Google Cloud Platform. The role focuses on ingesting, storing, processing, analyzing, exploring, and visualizing data, with involvement in migrations, transformations, and data pipeline delivery.

Location and Role Details

• Location: Seattle, WA (onsite)
• Travel: Up to 30% of the time for in-region meetings, technical reviews, and onsite delivery activities
• Compensation: USD 130,000 - 187,000 per year
• Experience level: Minimum 5 years

Key Responsibilities

• Serve as a trusted technical advisor to customers and address complex Big Data and AI challenges.
• Develop and deliver best practice recommendations, tutorials, blog articles, sample code, and technical presentations, adapting the approach and messaging to different business and technical audiences.
• Assess on-premises and cloud database environments and consult on optimal design decisions for performance and deployment on Google Cloud Platform.
• Provide clear communication via video conferencing for meetings, technical reviews, and onsite delivery activities.
• Collaborate with customers to implement Google Cloud products while supporting data ingest, transformation, and pipeline work.

Required Qualifications

• Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
• 5 years of experience with data processing software (for example, 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 experience writing software in Java, Python, or SQL.

Relevant Technologies

• Spark, Beam
• Java, Python, SQL
• 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 technical architectures, infrastructure components, ETL/ELT, and reporting or analytic tools and environments.
• Experience building production LLM/GenAI systems, including multi-agent orchestration, agent frameworks (for example, ADK), tool and function calling, RAG, and prompt or context development.
• Experience architecting and developing software or internet-scale production-grade Big Data solutions in virtualized environments.

Benefits

• Health, dental, vision, life, and disability insurance
• 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
• Maternity Leave (Short-Term Disability + Baby Bonding): 28 to 30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year