Senior Analytics Engineer

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

• Own and continuously improve the go-to-market data model, establishing consistent definitions for accounts, activated workspaces, pipeline, and other critical business metrics.
• Build production-grade analytics models in dbt with appropriate testing, documentation, CI, version control, and deployment practices.
• Model the complete self-serve funnel, from first website interaction through signup, activation, paid conversion, and expansion across multiple products.
• Develop comparable cohorts and funnel reporting that clearly identifies conversion drop-offs and opportunities for improvement.
• Model the sales funnel from acquisition source through MQL, SQL, pipeline, and closed-won, using real-world Salesforce data and accounting for data-quality challenges.
• Build and maintain attribution models spanning first-touch, last-touch, and multi-touch approaches, clearly documenting assumptions and methodologies.
• Develop unit economics reporting covering CAC, payback, LTV/CAC, campaign ROI, event ROI, and comparisons between self-serve and sales-assisted motions.
• Source and integrate marketing and campaign spend data that may not yet be available in existing reporting systems.
• Build and maintain a scalable reporting and semantic layer in Omni, Looker, or a comparable analytics platform so go-to-market teams can answer questions independently.
• Support recurring go-to-market operating rhythms, including pipeline reviews, weekly funnel meetings, and executive or board reporting.
• Partner directly with Marketing, Sales, Revenue Operations, Product, and Growth leadership to translate business questions into meaningful analysis and actionable metrics.
• Proactively identify underlying data issues, inconsistencies, and gaps, and determine how they should be measured or resolved rather than simply waiting for predefined requirements.
• Work with product analytics, CRM, marketing automation, and customer data systems to ensure reliable data flows and usable metrics.
• Use Python for API integrations, spend-data ingestion, enrichment, and analytical tasks where SQL is not the right tool.
• Leverage AI-native development practices and identify opportunities to make analytics engineering workflows faster, more reliable, and more efficient.

• 4+ years of experience building and maintaining analytics models in dbt on a cloud data warehouse, with ownership of production workflows, testing, CI, and version control.
• Strong SQL skills and practical Python experience for API pulls, data ingestion, enrichment, and specialized analytical tasks.
• Experience building and maintaining a semantic layer in Omni, Looker, or a comparable analytics platform, with an emphasis on creating reusable metrics for business users rather than simply building dashboards.
• Direct experience partnering with go-to-market teams on funnel conversion, attribution, unit economics, CAC, payback, ROI, and related growth metrics.
• Hands-on experience working with CRM data from Salesforce, HubSpot, or comparable systems.
• Familiarity with go-to-market technology ecosystems including Salesforce, Amplitude, Segment, HubSpot, and related platforms.
• Experience in a product-led, self-serve, or usage-based business, with an understanding of activation, trials, PQLs, product usage, and conversion.
• Engineering or data-focused background with the ability to write production code, understand existing codebases, and deploy owned work.
• Strong understanding of data modeling, metric definitions, data quality, and analytical infrastructure.
• Comfort working with imperfect or inconsistent data and the persistence to investigate the underlying causes rather than relying on idealized schemas.
• Strong business judgment and communication skills, with the ability to turn open-ended questions into clear analytical approaches and actionable recommendations.
• Ability to work independently as a senior individual contributor while influencing how teams use and understand go-to-market data.
• Comfort with AI-native development and a proactive approach to using AI tools to improve engineering productivity and analytical workflows.
• Experience with product analytics platforms, particularly Amplitude, is a plus.
• Experience working with developer tools, open-source products, or community-driven growth models is a plus.

• Competitive location-based base salary: $173,000–$222,000 for the San Francisco Bay Area and NYC Metro; $162,000–$201,000 for Washington, D.C., Boston, Los Angeles Metro, and Seattle; $154,000–$200,000 for Denver, Chicago, Atlanta, and other U.S. metropolitan areas.
• Equity stock options.
• 401(k) plan with a 5% company match, with immediate vesting.
• Unlimited paid time off.
• Medical, dental, and vision insurance.
• Generous parental leave.
• Life insurance and disability benefits.