Senior Analytics Engineer, Data Architecture & Governance

This is not a people management role and it is not a front-end dashboard or visualization role. The Data Team is responsible for creating the certified data foundation that downstream teams use for reporting analysis and self-service. This role will be accountable for designing and maintaining that foundation ensuring the data is accurate well-structured clearly documented and connected to how the business actually operates.

A critical part of this role is connecting the dots. You will work across Sales Marketing Finance Revenue Operations Product and other business teams to understand how processes systems data and KPIs fit together. You will translate that understanding into scalable data models clear definitions and documentation that helps the business consume data consistently and safely.

- Build and Own Core Data Models

Design build and maintain scalable data models in BigQuery using dbt. Create certified datasets tables and views that downstream teams can use confidently for reporting analytics and decision-making.

- Serve as a Senior Individual Contributor

Operate as a hands-on technical contributor responsible for solving complex data modeling business logic and data architecture challenges. This role does not manage a team. Success depends on strong ownership technical judgment collaboration and the ability to independently drive work from business question to documented data solution.

- Connect the Dots Across the Business

Work across Sales Marketing Finance Revenue Operations Product and other teams to understand how business processes systems data flows and KPIs connect. Identify where definitions processes or source-system logic are misaligned and help create a clear consistent data structure that reflects how the business operates.

- Leverage AI Securely to Optimize and Move Faster

Use approved AI tools automation and modern data practices to improve productivity accelerate documentation surface patterns validate logic and reduce manual effort. Apply AI in a secure and controlled manner ensuring sensitive data is protected and that outputs are reviewed validated and aligned to company governance standards.

- Define and Standardize Business KPIs

Partner with business stakeholders to define document and standardize key business metrics across the data layer. This includes SaaS and subscription metrics such as ARR MRR churn net revenue retention gross revenue retention expansion revenue bookings pipeline conversion rates and customer lifecycle metrics.

- Model the Sales and Revenue Funnel

Build data models that support the full go-to-market motion including lead generation marketing attribution campaign performance opportunity progression pipeline creation bookings renewals expansion and retention. Ensure the data architecture connects marketing activity sales motion customer activity revenue outcomes and business KPIs in a clear and consistent way.

- Own Documentation and Data Definitions

Create and maintain high-quality documentation for datasets fields transformations business rules metric definitions lineage assumptions and known limitations. Documentation is a core responsibility of this role. Downstream users should be able to understand what the data means where it comes from how it is calculated when it should be used and where caution is required.

- Translate Business Logic into Data Architecture

Take complex business requirements and translate them into clean SQL logic scalable schemas reusable dbt models and clear documentation. Help stakeholders understand tradeoffs dependencies and implications when business rules systems or reporting requirements change.

- Strengthen Data Governance and Quality

Support data governance by enforcing modeling standards naming conventions testing validation documentation and certification of production-ready datasets. Proactively identify data quality issues inconsistent logic gaps in source systems and downstream reporting risks. Partner with the appropriate teams to resolve issues at the source where possible.

- Partner on Data Platform Best Practices

Collaborate with the Senior Data Architect and broader Data Team on architecture decisions modeling standards dbt best practices platform optimization and long-term data strategy.