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.
2 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.
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
2 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.
