Stand up 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 and 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 experience writing code in Python and SQL.
Experience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, 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 building LLM applications (e.g., Gemini, RAG, vector search) and multi-agent frameworks (LangGraph, CrewAI, ADK) using advanced patterns (ReAct, delegation).
Experience with GCP data products (BigQuery, Spark, Spanner, Dataplex) or equivalents (Databricks, Snowflake), and open table formats (Iceberg, Delta Lake).
Experience migrating analytics, Spark, or database workloads between platforms with measured cost and performance outcomes.
Skilled in prompt engineering, model evaluation, LLM-native metrics (latency, tokens/sec, cost), state management, and granular tracing.
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 experience writing code in Python and SQL.
Experience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, 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.
