Senior Staff Engineer - Data Engineer
Summary
Senior staff-level data engineer focused on building and maintaining SQL data transformations with dbt (models, macros, tests, snapshots, lineage) on a major cloud data platform such as Snowflake, Databricks, BigQuery, or Redshift, while championing AI-assisted development with tools like Claude Code and GitHub Copilot.
We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (15000+ experts across 26 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!
Job Description
- 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:
- Snowflake
- Databricks
- Google BigQuery
- Amazon Redshift
- Daily, fluent use ofClaude Codeand/orGitHub Copilotfor 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
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