Design, build and operate scalable data pipelines, data models and end-to-end solution architectures on GCP (BigQuery, Python, dbt, Airflow, Dataform), balancing reliability, maintainability, performance and cost.
Own technical delivery on your projects and accounts, from scoping to production: making the design decisions, managing risks and dependencies, and communicating trade-offs clearly.
Own the quality of what we deliver, with rigorous validation, automated testing, observability and data-quality controls built into production pipelines, and lead the diagnosis and fix when production issues come up.
Design and build AI applications for clients and for fifty-five, such as LLM agents on Google Cloud with Gemini and Google's ADK. Take them from prototype to production, with evaluation, cost control and secure handling of client data.
Use agentic coding tools such as Claude Code or Codex to build faster and improve testing, checking their output critically and staying accountable for the result. Share what works with the team as demos, docs and reusable patterns.
Work with clients, consulting and data science teams to clarify requirements, challenge assumptions and turn business needs into pragmatic designs, and explain your reasoning to technical and non-technical people.
Raise the team's engineering standards through design and code reviews, strong testing practices and clear documentation.
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