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Mid-level requirement, broad skillset, and a common analytics-engineer title drive high candidate competition.
Core analytics engineering skills transfer across industries, though publishing domain knowledge is beneficial.
Explicit 5+ years plus mandatory Snowflake/dbt/SQL, BI and governance skills create strict filters.
Develop scalable, trusted data products including transformation pipelines, dimensional models, and reusable datasets across publishing, sales, marketing, finance, and operations.
Define and maintain consistent business metrics and logic in partnership with business leaders to enable shared data definitions across dashboards, analyses, and AI tools.
Implement data quality controls, documentation, monitoring, automation of manual processes, and responsible AI practices to improve reliability, decision speed, and self-service analytics adoption.
5+ years of experience in analytics engineering, data engineering, BI engineering, or related data roles.
Advanced SQL, dimensional modeling skills, and experience with structured and semi-structured data.
Hands-on experience with modern cloud data platforms and transformation frameworks such as Snowflake and dbt or equivalents.
Practical Python skills, experience with Git, modular design, testing, code review, CI/CD, and production support.
Experienced in building governed, scalable analytics products and modernizing legacy reporting into self-service solutions.
Comfortable partnering broadly across business functions and translating ambiguous problems into actionable data solutions.
Able to lead technical standards and mentor team members to raise analytics engineering maturity.