Lead Analytics Engineer

📋 Description

• • As the Lead Analytics Engineer at Kit Global Inc., you will be instrumental in shaping the future of data-driven decision-making for a rapidly growing creator-focused operating system. Kit is at a pivotal moment, and your role is to architect and build the foundational data layer that empowers every team—Finance, Product, Marketing, and Revenue—to operate with a single source of truth. This is a critical individual contributor (IC) role for a systems-thinker who excels at transforming ambiguous metric definitions into reliable, well-documented, and auditable data models.
• • Your primary objective will be to elevate the company's analytical capabilities, ensuring that teams can confidently explore data independently, reducing reliance on the Data team for routine interpretation and analysis. You will report to Samuel Umachi, Head of Data, and collaborate closely with an existing analytic engineer, infrastructure engineers, and cross-functional partners across Product, Engineering, Finance, Marketing, Sales, and Creator Growth.
• • In your first week, you will immerse yourself in Kit's environment by completing onboarding in Notion, meeting your new colleagues through introductory calls, and familiarizing yourself with core tools such as dbt, Github, Redshift, Omni, Slack, and Linear. You will also begin reviewing existing documentation related to the Reporting Hub architecture and canonical metric definitions.
• • Over the first month, your focus will shift to auditing active Reporting Hub models across key business verticals including Finance, Marketing, Sales, Product Strategy, and Creator Lifecycle. You will meticulously map current churn logic implementations, identify inconsistencies in canonical metric definitions, and assess performance bottlenecks within Redshift. This will culminate in publishing a comprehensive architectural assessment memo outlining your initial findings and prioritized recommendations. You will also serve as the primary data team representative in cross-functional discussions concerning metric definitions, attribution logic, and overall analytical rigor.
• • Within the first six months, you are expected to propose and commence the execution of a strategic 6–12 month modernization roadmap for the data modeling layer. This will involve refactoring high-risk models across all business verticals, clarifying attribution model contracts, and enhancing cross-functional documentation standards. A key deliverable will be measurably reducing warehouse inefficiencies through optimization of distribution and sort keys, rather than solely relying on infrastructure scaling. You will also improve the structure of Segment event modeling, aligning event design with reporting needs in partnership with Product and Engineering. A significant part of this phase involves driving company-wide adoption of canonical metrics by collaborating directly with functional leads to replace ad hoc definitions with documented, auditable standards.
• • By the end of your first year, you will have established a robust Reporting Hub foundation across all business verticals, ensuring consistent enforcement of canonical metrics. This will enable true stakeholder self-serve capabilities, allowing teams to explore data autonomously. You will proactively reduce model rework stemming from upstream ambiguity by implementing effective upstream design patterns and comprehensive documentation. Furthermore, you will elevate the overall modeling sophistication of the Data team through mentorship, the establishment of best practices, and the promotion of shared tooling. The leadership team will leverage this trusted, unified metric layer for informed resource allocation and growth decisions. Finance operations will become more efficient with reliable and consistent revenue and subscription models. Product and other departments will make critical decisions based on accurate cohort and performance data, eliminating the need for manual verification and allowing the Data team to deliver faster with reduced rework.
• • This role demands a deep understanding of SQL, with the ability to write readable, performant, and maintainable queries, coupled with a profound grasp of relational semantics. Expertise in Redshift, including its performance tuning mechanisms like sort keys and distribution styles, is crucial. Fluency in dbt for building modular, well-documented code is essential, as is a strong capability in business modeling to translate complex logic into trusted data models. Excellent written communication skills for producing clear documentation and memos, strong analytical judgment for navigating metric disputes, and the ability to translate complex data concepts for diverse stakeholders are paramount. You will also be expected to actively utilize AI tools to enhance productivity while maintaining critical oversight.
• • You will thrive in this role if you think in systems, are patient with complexity, and believe in documenting work as it happens. You are comfortable pushing back when necessary to uphold analytical rigor and operate with extreme autonomy, identifying needs and driving solutions without constant direction. This position is ideal for someone who sees their role as amplifying the effectiveness of the entire data team and driving significant company-wide impact through leadership by example.