Senior Data Analyst, GTM Analytics

Accountabilities

• Deliver decision-ready analysis across Sales, Marketing, and Client Success, covering pipeline health, funnel conversion, campaign and channel performance, cohorts, retention, and GRR/NRR drivers.
• Partner directly with business stakeholders to define analytical questions, develop recommendations, and carry insights through to business decisions rather than simply delivering reports.
• Translate ambiguous stakeholder requests into structured, testable analytical approaches with clear objectives and measurable outcomes.
• Build and maintain dbt models and data marts supporting Sales, Marketing, and Customer Success, using Salesforce as a core source system.
• Own data quality and documentation for assigned domains, establishing reliable definitions, source-of-truth datasets, and quality assurance practices.
• Contribute to the development of a BI-as-code environment, helping define how dashboards, metrics, and semantic models are created, versioned, reviewed, and maintained.
• Help advance agentic analytics workflows by using AI agents to accelerate analysis, automate recurring reporting, and reduce manual dashboard requests.
• Evaluate where AI can effectively replace or augment manual analytical work while maintaining appropriate human oversight and analytical rigor.
• Use Python, SQL, dbt, and AI-assisted development tools to automate repetitive tasks and improve analytical efficiency.
• Manage an individual analytics backlog within two-week sprints, aligning priorities with team objectives and communicating progress proactively.
• Collaborate closely with Analytics Engineering and GTM leadership to ensure analytical solutions are scalable, accurate, and directly connected to business priorities.

Requirements

• 5+ years of experience in analytics or business intelligence, including meaningful experience supporting Sales, Marketing, or Customer Success within a fast-growing organization.
• Demonstrated ability to own complex or ambiguous analytical problems end-to-end and provide strategic direction beyond routine reporting.
• Expert-level SQL skills and hands-on experience building and maintaining dbt models for go-to-market data.
• Strong experience working with Salesforce data, including familiarity with CRM objects, fields, relationships, and common data-modeling challenges.
• Comfortable using Python for analysis, automation, and eliminating repetitive manual workflows.
• Experience with modern data stacks, including tools such as Fivetran or comparable EL platforms and cloud data warehouses such as Snowflake or BigQuery.
• Familiarity with modern BI platforms such as Sigma, Hex, Tableau, Looker, or comparable tools is highly desirable.
• Understanding of governed semantic layers, self-service analytics, BI-as-code, and modern data organization practices.
• Genuine interest in agentic analytics and hands-on experience using AI agents or LLM-based tools to accelerate analytical work.
• Experience with Claude Code, Codex, or similar coding agents is a strong advantage.
• Strong communication skills, with the ability to explain analytical findings and recommendations clearly to non-technical stakeholders and confidently defend conclusions.
• Strong ownership and follow-through, taking responsibility for deliverables from initial question through final outcome.
• Comfortable operating in ambiguous, rapidly changing environments and delivering iteratively through short development cycles.

Benefits

• Competitive salary: $145,000–$175,000 per year.
• Remote work: Fully remote position based in the United States.
• High-impact analytics: Direct influence over Sales, Marketing, and Client Success strategy and decision-making.
• Modern data environment: Opportunity to work with SQL, dbt, Salesforce, cloud data infrastructure, BI-as-code, and modern analytics practices.
• AI-forward culture: Hands-on opportunity to help develop agentic analytics workflows and integrate AI into everyday analytical processes.
• Cross-functional collaboration: Close partnership with business leaders and analytics engineering teams.
• Fast-paced delivery: Work within two-week sprints where analytical projects move quickly from concept to implementation.
• Professional growth: Opportunity to shape an evolving analytics function and establish scalable standards for GTM data and decision support.