Staff Engineer - Data Analyst

On-siteStaffAnalytics Engineerposted 1w ago

✦Role summaryAI-generated

This staff engineer role focuses on building and maintaining production‑grade dbt pipelines across multiple cloud data platforms, ensuring data quality and lineage. Candidates must bring deep SQL expertise, hands‑on dbt experience, and proficiency with Snowflake, Databricks, BigQuery, or Amazon Redshift.

Skills required

dbt SQL Snowflake Databricks BigQuery Amazon redshift Jinja Data quality Data engineering Data transformation AI LLMs Agentic AI

About this role

6+ 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, including
- dbt models and materialization
- Incremental models
- Macros and Jinja
- dbt tests and data quality frameworks
- Snapshots
- Seeds and sources
- Documentation and lineage
- dbt packages
Strong experience with at least one cloud data platform, such as:
- Snowflake
- Databricks
- BigQuery
- Amazon Redshif

AI skills (required for all roles)

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