Analytics Engineer III

Sovos is a global leader in tax compliance solutions, committed to transforming how businesses navigate regulatory challenges. As an Analytics Engineer III, you will own the business layer that connects data pipelines to finance teams, ensuring accurate financial metrics and serving as a liaison between data and finance.

Responsibilities:

• Own the business-layer dbt models that finance teams query directly, sitting on top of the Senior Data Engineer’s pipeline infrastructure
• Partner with the Finance Analyst to define and codify ARR, NRR, GRR, churn, and deferred revenue metric logic in dbt
• Validate dataset outputs against NetSuite actuals and the Finance Analyst’s source of truth prior to close sign-off
• Serve as an embedded partner in the financial close cycle – proactively flag anomalies and surface exceptions before they become close-day problems
• Act as the primary liaison between data and finance: translate business questions into data models, and translate data outputs back into finance language for FP&A, Accounting, and OTC stakeholders
• Own the data catalogue and documentation for all financial models, maintaining a clear source of truth for what each metric means and how it is calculated
• Extend business-layer metric definitions to cost-side data (OPEX, COGS, commissions, ASC 340-40 capitalized software) as scope expands
• Upskill into MCP/AI engineering in Phase 2 – build MCP server tool schemas, own AI-assisted ARR and revenue forecasting use cases, and manage the prompt library and evaluation framework that determines AI answer quality for finance use cases

Requirements:

• 3–5 years of experience in data analytics or data engineering, with demonstrated ownership of models used in production by finance or operations teams
• Strong SQL skills: CTEs, window functions, complex joins, and the ability to diagnose why a financial model doesn't match expectations by reading the query
• Production dbt experience – will work directly in the team's dbt codebase from day one; equivalent frameworks are not sufficient
• Fluency in the revenue data chain (Billing → ARR → Deferred Revenue → Rev Rec → Close Pack) with the ability to debate reconciliation issues directly with a Finance Analyst
• Lived experience working with or embedded in a Finance, FP&A, or RevOps team – understands what close means, why timing matters, and what a board pack is for
• Working knowledge of ASC 606: understands why deferred revenue exists and how recognition timing works well enough to model revenue recognition logic without constant guidance
• Proactive approach to data quality: flags issues before they surface in close, and communicates clearly in both data and finance language
• Demonstrated curiosity and appetite for upskilling – has picked up a new technical skill on the job and shipped something with it; genuine interest in growing into AI/MCP engineering in Phase 2
• Solid data modeling fundamentals: star schema, SCDs, fact vs. dimension tables, and data quality testing patterns
• Python for scripting, data processing, and API integration
• Experience with analytics or BI tools (Tableau, QuickSight, or equivalent) for consuming and validating the reporting layer
• Background in B2B SaaS metrics: ARR, GRR, NRR, pipeline, utilization, and invoice aging
• Strong accountability mindset – checks whether the numbers are right before anyone asks
• Ability to define what “correct” looks like for a given metric and write it down as a testable assertion