Forward-Deployed AI Data Engineer (Enterprise Cloud)

Google Inc.; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Google Cloud is seeking Forward Deployed Engineers (Data and AI) to embed with major customers and take Google’s data and AI stack from design to production-scale workflows. You will work with BigQuery, Dataproc, Dataflow, Dataform/dbt, and Gemini for Data to enable Text-to-SQL and intelligent analytics at enterprise scale.

You will partner with customer engineering teams to implement robust data architectures, governance, and evaluation harnesses, delivering measurable ROI and scalable MLOps

Qualifications

• Bachelor’s degree in Engineering, Computer Science, or equivalent practical experience.
• 8+ years of software development and data engineering with SQL, Python, Java, Scala, or Go.
• Experience with ETL/ELT frameworks (dbt, Dataform) and designing enterprise data modeling layers or data marts.

Responsibilities

• Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade workflows.
• Co-build with customer engineering teams to instill Google-grade development practices.
• Design and build high-throughput data pipelines and utilities for large evaluation datasets.
• Construct scalable ETL/ELT pipelines to design enterprise data marts and semantic models.
• Create mechanisms for large-scale synthetic data generation with referential integrity.

Skills

8+ years exp

SQL

Python

Java

Scala

Go

ETL/ELT

Data modeling

Data pipelines

Education

Bachelor’s degree in Engineering or Computer Science

Master’s degree or PhD (preferred)

Tools

dbt

Dataform

BigQuery

Dataproc

Job description

Google Cloud is seeking Forward Deployed Engineers (Data and AI) to embed with major customers and take Google’s data and AI stack from design to production-scale workflows. You will work with BigQuery, Dataproc, Dataflow, Dataform/dbt, and Gemini for Data to enable Text-to-SQL and intelligent analytics at enterprise scale.

You will partner with customer engineering teams to implement robust data architectures, governance, and evaluation harnesses, delivering measurable ROI and scalable MLOps