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# Analytics Engineer, Finance & Modeling

- **Company:** TALENTHOP LLC
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $100,000.0 - $120,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Data Mart, Data Warehousing, Python (Programming Language), SQL Databases, Transaction Data, Git
- **Published:** September 18, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pl4pi7l97a

## About the Role

* Minimum of 3 years experience in analytics engineering, data science, BI engineering, or advanced analytics.
 * Experience with statistical modeling applied in business contexts, such as forecasting, survival analysis, cohort analysis, or propensity modeling.
 * Ability to interpret and communicate results from price changes or growth experiments objectively.
 * Proven experience developing and defending subscription financial metrics to stakeholders.
 * Strong proficiency in SQL and dimensional data modeling, with experience designing data marts.
 * Regular use of Python and Git in daily work.
 * Familiarity with AI tools and coding agents in practice.

## Description

The Analytics Engineer, Finance & Modeling is responsible for managing and enhancing the financial metrics, reporting, and reconciliation processes that support decision-making within the finance team. This role is critical in transforming subscription and transaction data into accurate, defensible financial insights and advancing forecasting and modeling capabilities. The position contributes by creating automated, reliable financial data systems that enable informed business planning and resource allocation., * Define and govern key financial metrics such as ARR, renewal rate, churn, and CAC, ensuring they are versioned, tested, and documented.

 * Develop automated financial reports and transition recurring finance reporting to scheduled, self-service processes.
 * Reconcile transactions across multiple platforms, implementing automated checks and escalating discrepancies with supporting evidence.
 * Build forecasting models for subscriber cohorts, revenue projections, retention, pricing impact, and churn propensity, integrating them into business planning.
 * Deliver models as tested, documented, and queryable tables within the data platform, avoiding reliance on notebooks.
 * Support a range of tasks including modeling, data reconciliation, and assisting other team members as needed.


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