Sr. Data Analyst Sales

TL;DR

Sr. Data Analyst Sales: Developing analytical solutions to improve reporting systems, data quality, and processes for the Sales and Revenue Operations (RevOps) teams with an accent on providing strategic recommendations that directly influence business decisions and company strategy. Focus on building data models behind operational metrics using SQL and creating visually compelling dashboards in Tableau.

Location: Remote - United States

Company

hirify.global is the data platform company for the AI era, building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference.

What you will do

• Translate high-level business problems into specific, measurable analytics, metrics, and visualizations.
• Lead analytical projects end-to-end, including defining scope, methodology, execution, and delivery.
• Provide guidance and analytical support to junior analysts and conduct peer review of code, analysis, and reports.
• Apply advanced SQL skills to extract and transform data, ensuring data integrity and accuracy.
• Develop and maintain automated Tableau dashboards that provide real-time visibility into the health of Sales.
• Act as a primary liaison between the analytics team and business function, influencing senior leaders and driving cross-functional alignment.

Requirements

• Masters degree in Business, Business Analytics, Computer Science, Data Science or a related field preferred.
• 5+ years as a data analyst with experience in sales analytics, commercial operations, FP&A, or business intelligence.
• Proven expertise in advanced SQL for data extraction and manipulation.
• Expert in Tableau for dashboard creation, data visualization, and data storytelling.
• Knowledge of SFDC data, schemas, and processes.
• Minimal travel required (approximately 10% or less annually).

Culture & Benefits

• Deliver quickly on requests, shortening development cycles and iterations.
• Ensure accuracy and reliability in findings that leads to trust and credibility with stakeholders.
• Break down complex problems, identify patterns, and derive meaningful insights from data.
• Convey complex data insights clearly and effectively to both technical and non-technical stakeholders.
• Manage multiple projects and deadlines with prioritizing tasks.
• Thrive in a culture of transparency and direct feedback.