Forward Deployed Engineer III, Data Cloud, Google Cloud - Singapore

GOOGLE ASIA PACIFIC PTE. LTD.; Singapore; Presencial; SGD 180.000 - 280.000; Tempo integral

Resumo da vaga

Google Cloud's Data Cloud team seeks a seasoned data engineer to build and ship data and ML workloads for customers using PySpark, SQL, and Python across BigQuery, Spanner, and AlloyDB.

You will architect lakehouse data foundations, implement robust access controls, and de-identify sensitive data for analytics projects. Strong ML deployment experience is valued.

Qualificações

• Bachelor's degree in Engineering, Computer Science, or related field; equivalent practical experience.
• 5 years of experience in software/data engineering, ML, or technical consulting, with coding in Python and SQL.
• Experience with data cloud ecosystem products (BigQuery, Spark, Spanner, Data Catalog, AlloyDB).
• Experience taking production-grade data cloud solutions from conception to launch, including cloud architectures (GCP).
• Experience training, evaluating, and deploying ML models in production.

Responsabilidades

• Build and ship production data and ML workloads with customers using PySpark, SQL, and Python on Google Cloud.
• Present working proofs with customer data, including de-identification of sensitive data.
• Build data foundations for agentic solutions, lakehouse architectures, metadata, and access controls.
• Train, evaluate, and deploy ML components to improve customer business metrics with monitoring.
• Refactor and migrate high-cost analytics and workloads to Google Cloud with cost-performance awareness.

Conhecimentos

Python

SQL

Data engineering

Machine learning deployment

Cloud architecture

Formação

Bachelor's degree in Engineering or Computer Science

Ferramentas

BigQuery

Spark / PySpark

Spanner

Data Catalog

AlloyDB

Gemini/LLM apps

Descrição da vaga

Product area

It's an exciting time to join Google Cloud's Go-To-Market team, leading the AI revolution for businesses worldwide. You'll excel by leveraging Google's brand credibility-a legacy built on inventing foundational technologies and proven at scale. We'll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We're a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era-the market is yours.

Job description

Google Cloud's Data Cloud team builds the data platform behind Google's agentic AI offerings: BigQuery, Managed Spark, borderless Lakehouse, Spanner, AlloyDB and Knowledge Catalog, integrated with Gemini Enterprise.

Job responsibilities

• Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL and Python on BigQuery, Managed Spark, Spanner and AlloyDB.
• Present working proofs with customers' data, including de-identifying sensitive data (e.g., PCI, PHI).
• Build the data foundation for agentic solutions, including lakehouse architectures, metadata, semantic context, and the access controls that let custom agents act on trusted data.
• Train, evaluate and deploy machine learning and agentic components where they improve the customer's business metric, with evaluation and monitoring in place.
• Refactor and migrate high-cost analytics, Spark and operational database workloads from other platforms onto Google Cloud engines, with measured price-performance results.

Minimum qualifications

• Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, including coding experience in Python and SQL.
• Experience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, Data Catalog, and AlloyDB).
• Experience taking production-grade data cloud solutions from conception to launch, including architecting data systems on cloud platforms (e.g., GCP) and building structured/unstructured pipelines.
• Experience training, evaluating, and deploying machine learning models in production.

Preferred qualifications

• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
• Experience in prompt developing, model evaluation, and the creative application of AI.
• Experience building applications on large language models (LLM) over enterprise data (e.g., Gemini, agents, RAG, vector search) and evaluating them for quality, latency and cost.
• Experience migrating analytics, Spark or database workloads between platforms with measured cost or performance outcomes.
• Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.