Senior Analytics Engineer / BI Product Engineer

About the Position

Senior Analytics Engineer / BI Product Engineer

LiftIQ is looking for a Senior Analytics Engineer / BI Product Engineer to build the reporting and analytics products our teams use to run the business. This is not a traditional BI role where stakeholders provide specifications and you simply build dashboards. We need someone who can understand the underlying business problem, determine what users actually need, and turn ambiguous requests into accurate, actionable analytics.

Often, the best solution will be different from what was originally requested. You should be comfortable challenging requirements, identifying insights users did not know to ask for, and designing reports that help people make decisions-not simply giving them more data. This is also a highly technical full-stack analytics role that blends analytics engineering with BI product engineering. You will work across BigQuery, dbt, TypeScript, Next.js, and React, using Claude Code extensively to accelerate development.

Data correctness is critical. Wrong numbers are worse than crashes. You will own the accuracy, reliability, and usability of LiftIQ’s reporting products end-to-end-from data modeling and metric definitions to dashboard experience and permissions.

Responsibilities

• Work directly with stakeholders to understand business questions and translate them into useful analytics products.
• Design metrics, reports, dashboards, and visualizations that surface actionable insights.
• Write sophisticated BigQuery SQL and build/extend dbt models, data marts, and dimensional models (facts, dimensions, semantic layers).
• Reconcile reporting against source systems and investigate data discrepancies.
• Build dashboards end-to-end across the data layer, semantic layer, application layer, permissions, and automated tests.
• Use Claude Code heavily while maintaining ownership of the underlying architecture, logic, and data correctness.
• Protect sensitive financial and compensation data through rigorous server-side access controls.
• Write automated tests for metrics, business rules, and permissions.
• Document business logic, metric definitions, and data models to ensure clarity and consistency across teams.

Requirements

• Essential

  • Exceptional SQL skills
  • Deep experience with analytical SQL, including complex joins, window functions, attribution, aggregation, data grain, reconciliation, and performance. BigQuery experience is strongly preferred.
  • Strong analytical and business judgment
  • Strong requirements-gathering skills
  • dbt experience
  • Data skepticism
  • Testing discipline
  • English: strong working proficiency (B2+), with the ability to communicate directly with business stakeholders and gather requirements.

• Nice to Have

  • Performance or affiliate marketing experience, including CPL, CPA, EPC, ROAS, and media-buying economics
  • Experience with platforms such as Everflow, Ringba, RedTrack, Voluum, LeadProsper, or HubSpot
  • GCP / Cloud Run / Cloud Scheduler / Cloud Build
  • Python ETL and API integrations
  • Payroll or commission systems experience
  • Claude API or other LLM application development
  • Interactive data visualization
  • Playwright / E2E testing

Tech Stack

• Data: BigQuery, dbt, Python ETL, Google Sheets
• Application: Next.js 14, TypeScript, React, Tailwind, NextAuth, Neon Postgres
• AI: Claude Code, Claude API
• Testing: Vitest
• Infrastructure: GCP, Cloud Run, Cloud Scheduler, Cloud Build, Docker

What Success Looks Like

A strong person in this role becomes someone the business brings problems to, not dashboard specifications. If someone says, “I need to understand why this buyer’s margins dropped,” you should be able to understand the business context, interrogate the data, identify the relevant metrics and potential causes, and build something that makes the answer clear.

You should be equally comfortable debugging a subtle SQL grain issue, talking with a stakeholder about how their business works, and using Claude Code to turn the resulting solution into a production dashboard.

This is a full-stack analytics role, but the hardest part of the job is data correctness, modeling discipline, and analytical judgment-not frontend development. Success also means stakeholders trust your numbers without hesitation, dashboards become decision tools rather than data dumps, and metric definitions remain consistent across teams.

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