Lead Analytics Engineer

The Knot Worldwide

4h•$134,865 - $231,198•Remote

About The Position

The Lead Analytics Engineer will architect data and analytics infrastructure that enables data science and analytics teams to work from shared, trusted foundations across Growth & Lifecycle, Marketplace, eCommerce, WeddingWire, and International. The role owns the business-context layer built on Data Engineering’s pipelines and the semantic layer that serves as a single source of truth. It partners with Data Engineering, Data Science, Analytics, and business leaders, and reports to the Senior Director of Data Science. This is a hands-on architect role focused on making analytics across the organization faster and more consistent. The role is fully remote and not tied to a specific office location. There are no regular in-office requirements, though remote team members are encouraged to gather intentionally for key company and team events.

Requirements

7+ years of experience building analytics infrastructure, data platforms, or data engineering at scale.
Expert-level data modeling skills, including dimensional modeling, star schemas, and fact and dimension tables.
Expert-level SQL skills and experience with a cloud warehouse such as Snowflake.
Expert-level experience with dbt and data pipeline orchestration such as Airflow or an equivalent.
Expert-level experience with semantic layers and BI tools such as Cube, Semantic Views, Preset, Looker, or equivalents.
A track record of building reusable datasets and standards adopted by multiple teams, and enabling AI tools for non-technical stakeholders.
Strong communication skills, including explaining complex data architecture to non-technical stakeholders and influencing without direct authority.
Systems thinking and an ability to optimize for organizational impact across teams rather than individual team wins.

Nice To Haves

Experience with experimentation infrastructure and test result analysis.
Open-source contributions or published writing on analytics architecture.
Experience coaching analytics engineers and data analysts.

Responsibilities

Design the shared data model and semantic layer so analytical teams can use common datasets rather than rebuilding them.
Define authoritative sources of truth for core business metrics and govern changes to metric definitions so teams and executives use consistent numbers.
Set and enforce standards for dataset structure, naming, documentation, testing, and Tier 1 versus Tier 2+ reporting, supported by team playbooks.
Architect a self-service layer that allows stakeholders to answer questions in BI tools without submitting data requests.
Partner with Data Engineering on the shared roadmap, data contracts, and warehouse performance and cost.
Own data and BI issues from root cause through resolution, escalating issues when they stall.
Coach Data Science and Analytics practitioners on dimensional modeling, semantic layer use, and self-service analytics.