Senior Analytics Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of product analytics and customer success analytics domains.

• Develop models to track product usage, feature adoption, and launch performance, including WAU, MAU, adoption depth, and cohort analysis post-launch.

• Create models for customer success focusing on customer health, onboarding, time-to-value, renewal risks, and expansion opportunities.

• Manage governed definitions for NRR, churn, and expansion, ensuring alignment with Finance reporting.

• Design semantic models that link usage behavior to customer outcomes and predict renewals.

• Enhance source-to-staging models and conformed dimensions developed by the data engineering team.

• Collaborate in shaping certification frameworks and modeling standards.

• Work alongside the Product team on event taxonomy and tracking plans.

• Implement models with alerting mechanisms to identify failures and drift.

• Keep documentation up to date regarding models, metrics, and definitions.

• Collaborate with Product and Customer Success stakeholders to transform inquiries into enduring models.

• Design and evaluate models in partnership with analysts.

• Develop internal AI agents, data-driven tools, and reusable skills.

• Create usable patterns in Omni and Hex.

• Define project requirements and manage projects throughout their entire lifecycle.

• Report directly to the Head of Data Engineering.

⛳️ Requirements

• Minimum of 5 years in analytics engineering, managing projects from inception to completion.

• Proficient in SQL, data modeling, and transformation, with expertise in dbt: advanced modeling patterns, macros, packages, and testing.

• Experience in building Slowly Changing Dimension (SCD) tables from various sources.

• Familiarity with Snowflake, dbt, and a semantic or BI layer such as Omni or Hex.

• Experience in modeling product usage and event data, as well as the instrumentation associated with it (e.g., Amplitude, Mixpanel, Pendo, or similar tools).

• Knowledge of modeling customer lifecycle and retention data, including health scoring, renewal risks, NRR, churn, and expansion, sourced from Customer Success platforms and support systems.

• Experience in designing semantic models or metric layers for both human and AI use.

• Ability to transform vague stakeholder inquiries into lasting models.

• Proficiency in AI-assisted development tools like Claude Code, including agentic pipeline design and skill-based workflows.

• Comfortable integrating tools through MCP servers.

• Strong communication skills with the ability to translate technical solutions into business language.

• Willingness to build solutions in areas lacking established playbooks.

• Experience in a regulated or high-sensitivity data environment is a plus.

• Experience where product usage data influenced retention or expansion efforts is desirable.

• Background in B2B SaaS, particularly in selling to small and mid-sized businesses or professional services firms, is a plus.

🏝️ Benefits

• Competitive Salary & Equity.

• 401(k) Program with Employer Matching.

• Health, Dental, Vision, and Life Insurance.

• Short Term and Long Term Disability coverage.

• Commuter Benefits (for in-office employees only).

• Autonomous Work Environment.

• Reimbursement for Workplace Setup.

• Telecommuting Stipend.

• Flexible Time Off (FTO) plus Holidays.

• Quarterly Team Gatherings.

• In-office Perks (for in-office employees only).