Enterprise Data & AI Solutions Engineer

Google LLC; New York, Cambridge (NY, MA); On-site; USD 127,000 - 184,000; Full time

Job summary

Google Cloud is seeking an Agentic Data Cloud Customer Engineer to act as the premier technical adviser for large accounts in the US. You will partner with Sales Specialists to execute pre-sales strategies, map legacy data estates for migration, and demonstrate end-to-end Data + AI pipelines that address enterprise needs.

Ideal candidates bring deep expertise in Python, PySpark, and distributed data systems, plus experience in building scalable data platforms in enterprise environments.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 6 years of experience as a data or systems engineer, solutions architect, or pre-sales consultant.
• Experience delivering demos, workshops, or architect overviews to business leaders.
• Experience migrating, refactoring, or debugging proprietary or open source workloads.

Responsibilities

• Define technical strategy for large accounts, engaging C-level executives via live demonstrations.
• Design, code, and deploy production-grade Data + AI pipelines and map legacy data estates to open-format architectures (Apache Iceberg).
• Orchestrate Intent-Driven data engineering to deploy PySpark/dbt pipelines.
• Lead showcases and technical value validations using BigQuery, Knowledge Catalog, Spark, and Vertex AI.

Skills

Python

PySpark

Distributed data systems

Data pipelines

Education

Bachelor's degree or equivalent

Tools

BigQuery

Apache Iceberg

dbt

Vertex AI

Knowledge Catalog

Job description

Google Cloud is seeking an Agentic Data Cloud Customer Engineer to act as the premier technical adviser for large accounts in the US. You will partner with Sales Specialists to execute pre-sales strategies, map legacy data estates for migration, and demonstrate end-to-end Data + AI pipelines that address enterprise needs.

Ideal candidates bring deep expertise in Python, PySpark, and distributed data systems, plus experience in building scalable data platforms in enterprise environments.