Analytics Engineer

About the Role

As a member of the Data team within the Go-to-Market organization, you will help build a data-driven culture, improve decision-making, and advance strategic initiatives through analytics.

This is a full-stack data role spanning data modeling, metric definition, visualization, analysis, and self-service tooling. You will build trusted, scalable data sources and products that give the business reliable, actionable insights. The work calls for judgment: you will choose the tool, approach, and level of investment that best fit each problem, from a focused analysis to a durable production data product.

As a core partner to the GTM organization, you will address both foundational and ad hoc analytics needs. You will turn complex data into clear narratives that help technical and non-technical audiences understand what is happening, why it matters, and what they should do next.

In This Role, You Will

• Partner closely with GTM teams to proactively identify high-impact questions and translate business needs into data models, metrics, analyses, and scalable technical solutions.
• Define, source, validate, and operationalize the metrics that guide the business, helping teams incorporate them into planning and day-to-day decisions.
• Lead cross-functional data projects across established and emerging business areas, including setting the data strategy for greenfield domains.
• Build scalable data models and pipelines that integrate and transform data from multiple sources into trusted, accessible datasets.
• Create dashboards, reports, analytical tools, and other data products that enable stakeholders to answer questions independently.
• Own the lifecycle of metrics, analytical models, and data products from initial exploration and prototyping through production and ongoing maintenance.
• Choose the most effective approach for each problem—whether an analysis, metric, data model, visualization, or self-service product—based on the audience, urgency, complexity, and expected value.
• Exercise strong judgment when prioritizing competing requests, balancing immediate business needs with investments that improve the long-term quality and scalability of the data ecosystem.
• Use AI-assisted development tools to increase productivity while maintaining clear, tested, and maintainable code, data models, and documentation.
• Turn complex findings into clear, persuasive narratives through presentations, written memos, dashboards, and other formats suited to the audience.