Senior Manager, Sales Data Management

About the Job

Red Hat’s Global Sales Go-To-Market Strategy, Incentives & Data Analytics team is seeking a Senior Manager, Sales Data Management to lead efforts in improving sales data quality, governance, and operational readiness across the organization. You will lead a team responsible for managing trusted sales datasets across Bookings Master, discounting, ecosystem/partners data, services data, incentives, planning, forecasting, quota modeling, territory planning, and performance measurement.

This strategic role is responsible for designing and implementing scalable data validation frameworks, automating data quality checks, driving proactive issue detection, and establishing governance-grade reporting standards. The role will ensure high accuracy, timely insights, consistent business definitions, and trusted data products that support critical GTM decision-making.

The successful candidate will combine strong people leadership, deep analytical expertise, technical fluency, and business acumen. You will play a pivotal role in advancing automation, standardization, visibility, and accountability across global sales and incentive processes. You will also serve as a key advisor to Sales, Operations, Finance, Incentives, Ecosystem/Partner, and Services stakeholders to enable business-critical decisions backed by trusted data.

Note: This role may come into contact with confidential or sensitive customer information requiring special treatment in accordance with Red Hat policies and applicable privacy laws.

What You Will Do

• Lead cross-functional initiatives to enhance data accuracy, timeliness, and consistency across Bookings Master, discounting, ecosystem/partners data, services data, incentives, planning, forecasting, and sales performance datasets

• Manage, coach, and develop a high-performing team of data analysts, data engineers, and business data professionals responsible for delivering trusted, governed, and business-ready sales data

• Set clear priorities, delivery expectations, operating rhythms, and accountability mechanisms to ensure the team delivers high-quality outcomes aligned to business priorities

• Design and implement validation rules, pipeline reconciliation dashboards, automated data quality checks, and exception reporting to ensure accounting-grade data integrity

• Enforce submission calendars, lead daily health checks, and establish monthly and quarterly lock processes to maintain trusted, auditable data workflows

• Standardize definitions, bookings classifications, discounting logic, partner attribution, services classifications, opportunity rules, and planning assumptions through collaborative reviews across GTM, Finance, Sales, Incentives, Ecosystem/Partner, and Services teams

• Deliver daily dashboards and reports to surface anomalies, drive stakeholder awareness, improve data visibility, and accelerate issue resolution

• Drive digitization of manual processes to reduce inefficiencies, improve repeatability, and enhance automation and operational scalability

• Maintain governance standards by ensuring compliance with accounting, audit, and finance requirements through proactive lock, review, reconciliation, and approval processes

• Curate and validate sales opportunity, bookings, discounting, ecosystem/partner, and services data for use in incentives, quota modeling, territory planning, forecasting, attainment, and executive reporting

• Analyze historical and in-quarter performance to identify gaps, anomalies, and risks across bookings, discounting, partner contribution, services performance, pipeline, attainment, and planning datasets

• Partner with dashboard and reporting teams to develop trusted KPIs and scalable tools supporting Bookings Master, discounting insights, ecosystem/partner analytics, services analytics, planning, attainment, incentives, and performance measurement

• Serve as a subject matter expert for sales data management, ensuring alignment on data use cases, reporting expectations, lock governance timelines, source-system alignment, and stakeholder accountability

• Partner with engineering and analytics teams to improve data lineage, observability, documentation, business rule versioning, and scalable data product delivery

• Leverage automation and AI-assisted approaches where appropriate to accelerate anomaly detection, data quality checks, contextual explanations, and recurring issue identification while preserving human accountability and authoritative source governance

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