Lead Analytics Engineer

Accountabilities: Design and maintain shared data models and semantic layers so analytical teams can work from trusted, reusable datasets. Establish authoritative definitions for core business metrics and govern how those definitions are maintained and updated. Define and enforce standards for dataset structures, naming conventions, documentation, testing, and reporting tiers. Develop the self-service analytics layer, enabling business stakeholders to answer questions independently through business intelligence tools. Partner with Data Engineering on shared roadmaps, data contracts, and data warehouse performance and cost optimization. Take ownership of data and BI issues from root-cause investigation through resolution, escalating blockers when necessary. Coach Data Science and Analytics teams on dimensional modeling, semantic layer adoption, and self-service analytics practices. Promote the use of AI tools to make analytics more accessible and effective for non-technical stakeholders. Requirements: 7+ years of experience building analytics infrastructure, data platforms, or large-scale data engineering solutions. Expert-level knowledge of data modeling, including dimensional modeling, star schemas, fact tables, and dimension tables. Advanced SQL skills and experience working with cloud data warehouses such as Snowflake. Expert-level hands-on experience with dbt and data pipeline orchestration tools such as Airflow or equivalent platforms. Strong experience with semantic layers and BI tools such as Cube, Semantic Views, Preset, Looker, or comparable solutions. Demonstrated success building reusable datasets, establishing technical standards, and driving adoption across multiple teams. Experience enabling AI tools and analytics capabilities for non-technical business users. Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders and influence decisions without direct authority. Strong systems-thinking mindset, prioritizing organization-wide outcomes over individual team objectives. Additional assets: Experience with experimentation infrastructure and test-result analysis. Open-source contributions or published content related to analytics architecture. Experience coaching analytics engineers and data analysts. Curiosity and sound judgment in adopting AI tools and applying them thoughtfully to improve work quality and efficiency. Benefits: Annual base salary range of $134,865–$231,198 USD, with starting pay determined by location, education, experience, skills, and other qualifications. Medical, dental, and retirement benefits for eligible employees. Generous leave policies. Fully remote work arrangement, with no regular in-office requirement. Opportunities to connect with colleagues through intentional company and team gatherings. Professional growth and development opportunities within a collaborative, international environment.