Lead Analytics Engineer

The Knot Worldwide is hiring an analytics engineering leader to architect shared data and analytics infrastructure for its data science and analytics teams. The role owns the business-context and semantic layers, establishes trusted metrics and data standards, and partners across data and business teams to enable consistent, self-serve analytics.

Responsibilities:

• Design the shared data model and semantic layer, so analytical teams reference shared datasets instead of rebuilding them
• Define the authoritative source of truth for core business metrics and govern how those definitions change, so executives see one set of numbers across teams
• Set and enforce standards for dataset structure, naming, documentation, testing, and Tier 1 vs. Tier 2+ reporting, with playbooks that teams follow
• Architect the self-serve layer that lets stakeholders answer their own questions in BI tools without filing a data request
• Partner with Data Engineering on the shared roadmap, data contracts, and warehouse performance and cost. Own data and BI issues end to end, from root cause to resolution, and escalate when they stall
• Coach Data Science and Analytics practitioners on dimensional modeling, semantic layer use, and self-serve analytics

Requirements:

• 7+ years of experience building analytics infrastructure, data platforms, or data engineering at scale
• Expert-level data modeling (dimensional modeling, star schema, fact and dimension tables) and SQL, with experience in a cloud warehouse such as Snowflake
• Expert-level experience with dbt and data pipeline orchestration (Airflow or equivalent)
• Expert-level experience with semantic layers and BI tools (Cube, Semantic Views, Preset, Looker, or equivalent)
• A track record of building reusable datasets and standards that multiple teams adopt, and of enabling AI tools for non-technical stakeholders
• Strong communication: you can explain complex data architecture to non-technical stakeholders and influence them without direct authority
• Systems thinking: you optimize for organizational impact across zones, not individual team wins
• experience with experimentation infrastructure and test result analysis, open source or published writing on analytics architecture, or coaching analytics engineers and data analysts