Sales Data Analyst

Mobile Communications America Inc•Arlington, TX

20d•Hybrid

About The Position

Mobile Communications America, Inc., a leader in wireless communications, data, and security solutions is seeking a data-driven Sales Data Analyst to join the Sales Strategy & Operations team within our Central Division. This position needs to be located within our footprint of Dallas, TX; Indianapolis, IN; Charlotte, NC; or Spartanburg, SC. This role turns sales data into decision-ready insight — building and improving the forecasting, pipeline, and performance analytics that guide how the business sells. The ideal candidate pairs strong quantitative and statistical skills with commercial fluency, and is comfortable working independently to translate complex data into clear, actionable recommendations for sales leadership.

Requirements

Bachelor's degree in a quantitative or analytical field (Statistics, Economics, Finance, Data Analytics, Business, or related); equivalent experience considered.
Approximately 3 years of experience in sales, revenue, or business data analysis, including hands-on forecasting and pipeline analytics.
Demonstrated experience applying statistical methods such as regression to commercial or sales data.
Strong proficiency in Excel and SQL for data extraction, modeling, and analysis.
Proven experience building dashboards and reports in Tableau (primary tool); experience with other BI platforms a plus.
Working knowledge of sales forecasting methodologies, funnel and pipeline metrics, and sales performance KPIs.
Solid statistical foundation — regression, correlation, trend, and variance analysis — with the judgment to match the method to the question.
Familiarity with CRM (HubSpot, NetSuite) and ERP (NetSuite, Ormandy) data structures.
Demonstrated use of automation and AI tools (e.g., scripting, workflow automation, or AI-assisted analysis) to improve the speed, quality, or scale of analytical work.
Excellent communication skills, with the ability to translate technical analysis into clear recommendations for non-technical sales leaders.
Strong attention to detail, intellectual curiosity, and the ability to manage multiple priorities independently.

Nice To Haves

Experience in technology, telecom, or services/reseller environments preferred; exposure to Python or R a plus.

Responsibilities

Measure and report forecast accuracy across teams, segments, and time horizons, identifying drivers of variance and recommending methodology improvements.
Develop, test, and maintain forecast weighting algorithms that translate pipeline stage, deal age, and historical conversion into reliable predictions.
Apply regression and other statistical methods to quoted and won/lost activity to surface the factors that most influence close rates and deal velocity.
Partner with sales leadership to refine forecasting methodology and strengthen commit and upside discipline.
Assess funnel sufficiency against quota and coverage targets, flagging gaps in pipeline volume, velocity, or conversion early enough to act.
Analyze stage-to-stage conversion, sales cycle length, and win rates across divisions, segments, and reps to identify performance trends.
Quantify pipeline health and recommend where to focus pipeline-generation and deal-progression efforts.
Build and maintain Tableau dashboards, scorecards, and recurring reporting that give sales leaders a clear, current view of performance.
Translate analysis into concise, executive-ready narratives and visualizations that drive decisions rather than simply describe data.
Leverage automation and AI tools to streamline data preparation, reporting, and analysis — reducing manual effort, accelerating turnaround, and improving consistency.
Identify opportunities to apply AI and automation to recurring analytical and reporting workflows, and help bring those improvements into production.
Source, validate, and reconcile sales data across CRM (HubSpot, NetSuite) and ERP (NetSuite, Ormandy) systems to ensure analyses rest on accurate, trusted data.
Partner with Sales Operations, Finance, and divisional leaders to align on definitions, metrics, and reporting standards.
Identify and resolve data quality issues, and recommend improvements to data capture and process.