8+ years of experience in Data Engineering, Analytics Engineering, or related fields.
3+ years of hands-on experience with dbt in production environments.
Strong expertise in SQL and complex data transformation development.
Strong understanding of dbt Core and/or dbt Cloud.
Experience with dbt models and materialization
Experience with incremental models
Experience building and maintaining macros using Jinja
Experience with dbt tests and data quality frameworks
Experience with snapshots
Experience with seeds and sources
Experience with documentation and data lineage
Experience using dbt packages
Strong experience with at least one cloud data platform: SnowflakeDatabricksGoogle BigQueryAmazon Redshift
Daily, fluent use of Claude Code and/or GitHub Copilot for implementation, refactoring,
test generation, and code review
Ability to establish team standards for AI-assisted development: effective prompting, trust-vs-verify discipline on generated code, security/IP guardrails, and reviewing AI-authored changes
Working understanding of LLM fundamentals: context windows, tokens, model selection, and prompt/context engineering
Experience integrating AI into developer workflows and agentic/automation tooling (MCP servers, AI-driven CI steps, codegen and doc-generation pipelines)
Able to evaluate AI tooling pragmatically: measuring real productivity and quality impact, not hype