Senior Sales Operations Analyst

Xometry•Lexington, KY

21h

About The Position

Xometry is seeking a seasoned analytics leader to own the Sales analytics function and serve as a strategic partner to Sales leadership and the broader executive team. The role turns complex and ambiguous data into insights that inform revenue decisions, owns the architecture and governance of the Sales dashboard and data ecosystem, develops quota and forecasting models, and establishes standards for analytics work. It operates with significant autonomy and involves direct engagement with VP- and C-level stakeholders.

Requirements

At least 5 years of experience in Sales/Revenue Operations, Business Analytics, or a related analytics function, with a strong focus on sales analytics, forecasting, or go-to-market strategy.
Advanced SQL proficiency, including complex joins, window functions, and query optimization.
Hands-on expertise in Looker and LookML, including data modeling and semantic layer design; Looker Studio is not specified as a substitute.
Advanced Excel or Sheets skills, including the ability to build and audit complex, multi-scenario financial and quota models from scratch.
Deep familiarity with CRM systems such as Salesforce and working knowledge of how sales data flows and can break or be misreported across systems.
Demonstrated ability to advise and influence senior VP- and C-level stakeholders using data, including constructively challenging expectations when the data indicates a different conclusion.
Strong executive communication and storytelling skills, including distilling complex analysis into clear narratives and recommendations for non-technical audiences.
Strong statistical literacy in trend analysis, forecasting methods, and cohort analysis, with judgment to select techniques appropriate to business questions.
Ability to independently source, assess, and prioritize data and competing requests in a fast-moving, ambiguous environment with shifting requirements.
Experience owning an analytics roadmap or reporting infrastructure end-to-end, including build-versus-deprecate decisions.
Experience reviewing or mentoring the work of other analysts.

Nice To Haves

Experience with dbt or a similar data transformation or modeling tool.
Working knowledge of Python or R for statistical modeling beyond what Excel or SQL can support.
Direct experience with sales quota design or compensation plan design.
Experience presenting directly to executive leadership or a board.
Experience partnering with Finance on revenue forecasting or annual planning cycles.
Prior experience managing or providing dotted-line mentorship to junior analysts.

Responsibilities

Act as the primary analytics partner to Sales leadership and senior executives by defining key metrics, surfacing strategic insights, and informing decisions on quotas, territories, headcount, and pipeline strategy.
Translate ambiguous business questions into structured analyses and identify relevant questions when leadership has not fully defined them.
Own the architecture, roadmap, and governance of Sales dashboards and reporting infrastructure, including decisions about what to build, deprecate, or redesign.
Design and refine sales performance and quota models using historical trends, pipeline data, seasonality, and representative-level variance to support quota setting, attainment tracking, and revenue forecasting.
Develop forecasting methodology using statistical techniques such as trend analysis, cohort analysis, and scenario and sensitivity modeling.
Set reporting standards and analytical best practices; review and improve other analysts' work and mentor junior team members on analytical methods, tools, and stakeholder management.
Proactively identify data risks, anomalies, and opportunities and bring them to leadership's attention.
Coordinate across Product, Marketing, Data Engineering, and Finance to resolve data discrepancies, align definitions, and support a single source of truth.
Lead or significantly contribute to data cleansing and data quality initiatives, including diagnosing upstream causes in the CRM and data pipeline.
Represent analytics in planning cycles, including annual quota setting, territory design, and compensation plan discussions, and provide data-backed recommendations.