Senior Analytics Engineer

Job description

**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.** As a Senior Analytics Engineer, you will own the data models and measurement infrastructure that power go-to-market decisions across self-serve and sales-led funnels. You will turn complex product, CRM, marketing, and spend data into trusted metrics that help teams understand growth and improve performance. The role combines hands-on analytics engineering with strategic partnership across Marketing, Sales, Revenue Operations, Product, and Finance. You will build reliable data models, semantic layers, attribution frameworks, and unit economics reporting in a modern cloud data environment. You’ll work with imperfect real-world data and be expected to investigate problems, define the right questions, and create practical solutions. As an individual contributor, you’ll have significant influence over how go-to-market data is modeled, measured, and used across the organization. ### Accountabilities: - 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. ## Requirements: - 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. ## Benefits: - 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. - **$800 per month remote-work stipend**. - Remote-first, flexible work environment. - Opportunity to work on modern data, analytics, automation, and AI infrastructure. - Significant autonomy and ownership as a senior individual contributor. - Opportunity to influence how go-to-market teams measure growth, performance, attribution, and economics. - Collaborative, high-performance culture that values ownership, thoughtful communication, continuous learning, and meaningful impact. - Inclusive environment designed to provide individuals and teams with access, opportunity, and the ability to contribute authentically.