Lead Analytics Engineer

The Knot Worldwide

. Architect the data and analytics infrastructure supporting data science and analytics teams across Growth & Lifecycle, Marketplace, eCommerce, WeddingWire, and International .

Posted 11/11/2026full-timeRemote • United StatesSenior💰 $134,865 - $231,198 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expert-level capabilities in data modeling, including dimensional modeling and semantic layers, while architecting data and analytics infrastructure to support diverse business needs. Proficient in SQL and cloud data warehousing, with a strong focus on building reusable datasets and enabling self-serve analytics for stakeholders.

Highest-signal resume keywords

Expert-Level Data ModelingExpert-Level SQLCloud Warehouse Experience (Snowflake)Data Pipeline Orchestration (Airflow)Expert-Level Experience with BI Tools

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Dimensional ModelingStar SchemaFact TablesDimension TablesData Pipeline OrchestrationSQLData ModelingReusable DatasetsSemantic LayersAnalytics Infrastructure

Soft Skills

Influencing Without AuthorityCoachingSystems ThinkingCommunication

Tools & Technologies

SnowflakeDbtAirflowCubeLookerSemantic ViewsPreset

Industry Keywords

Data EngineeringAnalyticsBusiness IntelligenceData ScienceGrowth & LifecycleMarketplaceECommerceData Governance

Tech Stack

Tools & technologies

AirflowCloudOpen SourceSnowflakeSQL

About the role

Key responsibilities & impact

Architect the data and analytics infrastructure supporting data science and analytics teams across Growth & Lifecycle, Marketplace, eCommerce, WeddingWire, and International
Own the business-context layer on Data Engineering pipelines and the semantic layer serving as the single source of truth
Design shared data models and semantic layers using shared datasets
Define authoritative sources of truth for core business metrics and govern changes to metric definitions
Set and enforce standards for dataset structure, naming, documentation, testing, and reporting tiers
Architect a self-serve analytics layer for stakeholders using BI tools
Partner with Data Engineering on roadmaps, data contracts, warehouse performance, and cost
Own data and BI issues from root cause through resolution and escalate stalled issues
Coach Data Science and Analytics practitioners on dimensional modeling, semantic layer use, and self-serve analytics
Report to the Senior Director of Data Science

Requirements

What you’ll need

7+ years of experience building analytics infrastructure, data platforms, or data engineering at scale
Expert-level data modeling, including dimensional modeling, star schema, fact tables, and dimension tables
Expert-level SQL
Experience with a cloud warehouse such as Snowflake
Expert-level experience with dbt
Expert-level experience with data pipeline orchestration, such as Airflow or equivalent
Expert-level experience with semantic layers and BI tools, such as Cube, Semantic Views, Preset, Looker, or equivalent
Track record of building reusable datasets and standards adopted by multiple teams
Experience enabling AI tools for non-technical stakeholders
Ability to explain complex data architecture to non-technical stakeholders and influence without direct authority
Systems thinking focused on organizational impact
Bonus: experience with experimentation infrastructure and test result analysis, open source or published writing on analytics architecture, or coaching analytics engineers and data analysts

Benefits

Comp & perks

Medical insurance
Dental insurance
Retirement benefits
Generous leave policies
Fully remote work with no regular in-office requirements
Intentional company and team gatherings
Meaningful opportunities for professional growth and development
Physical and mental wellness programs
Flexible time off
Virtual collaboration for roles not eligible for in-office expectations