Lead Analytics Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Lead Analytics Engineer based in the United States.

The Lead Analytics Engineer will architect and strengthen the shared data and analytics foundations that support decision-making across multiple business areas and international markets. This fully remote role combines hands-on technical architecture with strategic collaboration across Data Engineering, Data Science, Analytics, and business leadership. You will own the business-context and semantic layers that establish trusted, consistent definitions of key metrics and datasets. By creating reusable data models, clear standards, and scalable self-service capabilities, you will help teams work faster and reduce duplicated analytical effort. You will also influence the organization's data roadmap, improve warehouse performance, and help stakeholders make better use of AI-enabled analytics tools. Success in this position means making analytics more reliable, accessible, and consistent across the entire organization. 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.

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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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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