Senior Principal Engineer - Red Hat Sales Data Management (Raleigh Office)

Red Hat • Raleigh, NC

8d

About The Position

Red Hat’s Global Sales Go-To-Market Strategy, Incentives & Data Analytics organization is seeking a Senior Principal Engineer to work with a high degree of autonomy to lead the integration, automation, and optimization of complex data solutions. In this role, you will move beyond simple execution to provide technical leadership in data massaging, reconciliation, and architectural design. You will be responsible for building robust data pipelines, ensuring data governance, and collaborating with cross-functional teams to deliver high-quality data products that drive business decisions. What will you do? Enhance existing sales and renewals statistical models with AI-assisted contextual reasoning—without replacing proven methodologies Augment static sales business rules with configurable, explainable decision layers grounded in authoritative data Support customer- and territory-specific pattern recognition while maintaining statistical rigor Leverage Amazon Bedrock–powered LLMs or similar as an augmentation layer, not a system of record Apply RAG architectures to contextualize sales signals using trusted enterprise knowledge Automate repeated sales and renewals patterns using Salesforce-connected workflows Deliver field-facing transparency that explains how traditional signals and augmented insights work together Maintain audit-ready, compliant sales data and AI workflows aligned with incentive and finance governance Core ResponsibilitiesTechnical Leadership & ArchitectureOwn and evolve the end-to-end architecture for global sales systems, decision engines, and AI-augmented services Define standards for sales data modeling, service design, API contracts, event schemas, and hybrid AI integration Lead architectural reviews and make principled trade-offs across scalability, cost, governance, and explainability Act as a senior technical advisor across engineering, Sales Ops, Finance, Incentives, and Product Sales Data, Snowflake & GovernanceDesign and govern enterprise-grade sales and renewals data foundations (Snowflake or similar databases) Establish data quality, validation, reconciliation, lineage, and observability frameworks Implement accounting-grade submission calendars and lock processes (daily, monthly, quarterly) Ensure all sales decisions and AI-augmented outputs are traceable to authoritative sources Decision Systems, RAG & AI AugmentationArchitect hybrid sales decision systems combining statistical models, deterministic rules, and AI-assisted reasoning Design and implement Retrieval-Augmented Generation (RAG) to enrich—not override—traditional model outputs Leverage Openshift AI or Amazon Bedrock or similar to integrate foundation models in a secure, governed, non-authoritative role Use LLMs for contextual explanation, scenario analysis, and signal enrichment, not primary scoring Enforce guardrails such as source attribution, confidence thresholds, rule overrides, and human-in-the-loop controls Salesforce Integration & Intelligent AutomationArchitect systems that ingest and reason over Salesforce data to enhance renewals and pipeline models Automate sales validation, reconciliation, anomaly detection, and forecasting workflows Enable proactive identification of upsell, downsell, partial renewal, and other gaming risks Backend Services, APIs & Field-Facing EnablementArchitect backend services and APIs exposing trusted sales metrics alongside AI-augmented insights Enable UI experiences that provide clear explanations of decisions and recommendations to the field Ensure systems meet high standards for reliability, security, performance, and versioning Cloud, CI/CD & AI OperationsSet patterns for cloud-native architectures across sales data, backend services, and AI augmentation layers Establish CI/CD standards for sales pipelines, rule engines, and RAG workflows Define prompt versioning, evaluation, drift detection, and rollback practices Ensure AI augmentation is observable, controlled, and measurable Cross-Functional Influence & MentorshipPartner with Sales, Finance, Incentives, Product, and Operations leaders to align systems with business outcomes Mentor senior engineers on architecture, sales data rigor, and responsible AI usage Identify systemic risks and lead long-term remediation across global sales platforms What Sets This Role ApartYou modernize global sales data systems quality and standards without disrupting proven planning and business models You apply AI where it adds clarity, speed, and context, not authority You balance innovation with sales trust, governance, and auditability You influence global GTM outcomes through disciplined, state-of-the-art architectures What will you bring?

Requirements

• 10+ years of experience as a Data Engineer, BI Engineer, or Systems Analyst in an enterprise environment with large, complex data sources.
• Master’s degree in Computer Science, IT, Engineering, or equivalent experience.
• Deep expertise in relational databases (PostgreSQL, MSSQL, etc.) and query optimization.
• Strong programming skills for data querying, cleaning, and presentation, with hands-on experience in data-centric libraries.
• Ability to manage multiple projects simultaneously in a fast-paced, distributed team environment across different time zones and cultures.
• Exceptional logic and reasoning skills to troubleshoot complex data issues.
• Ability to think strategically about data architecture and project planning.

Nice To Haves

• Working knowledge of DBT (Data Build Tool) and Snowflake data warehousing is highly desirable.
• Experience with Fivetran or similar integration tools.

Responsibilities

• Own and evolve the end-to-end architecture for global sales systems, decision engines, and AI-augmented services
• Define standards for sales data modeling, service design, API contracts, event schemas, and hybrid AI integration
• Lead architectural reviews and make principled trade-offs across scalability, cost, governance, and explainability
• Act as a senior technical advisor across engineering, Sales Ops, Finance, Incentives, and Product
• Design and govern enterprise-grade sales and renewals data foundations (Snowflake or similar databases)
• Establish data quality, validation, reconciliation, lineage, and observability frameworks
• Implement accounting-grade submission calendars and lock processes (daily, monthly, quarterly)
• Ensure all sales decisions and AI-augmented outputs are traceable to authoritative sources
• Architect hybrid sales decision systems combining statistical models, deterministic rules, and AI-assisted reasoning
• Design and implement Retrieval-Augmented Generation (RAG) to enrich—not override—traditional model outputs
• Leverage Openshift AI or Amazon Bedrock or similar to integrate foundation models in a secure, governed, non-authoritative role
• Use LLMs for contextual explanation, scenario analysis, and signal enrichment, not primary scoring
• Enforce guardrails such as source attribution, confidence thresholds, rule overrides, and human-in-the-loop controls
• Architect systems that ingest and reason over Salesforce data to enhance renewals and pipeline models
• Automate sales validation, reconciliation, anomaly detection, and forecasting workflows
• Enable proactive identification of upsell, downsell, partial renewal, and other gaming risks
• Architect backend services and APIs exposing trusted sales metrics alongside AI-augmented insights
• Enable UI experiences that provide clear explanations of decisions and recommendations to the field
• Ensure systems meet high standards for reliability, security, performance, and versioning
• Set patterns for cloud-native architectures across sales data, backend services, and AI augmentation layers
• Establish CI/CD standards for sales pipelines, rule engines, and RAG workflows
• Define prompt versioning, evaluation, drift detection, and rollback practices
• Ensure AI augmentation is observable, controlled, and measurable
• Partner with Sales, Finance, Incentives, Product, and Operations leaders to align systems with business outcomes
• Mentor senior engineers on architecture, sales data rigor, and responsible AI usage
• Identify systemic risks and lead long-term remediation across global sales platforms

Benefits

• Comprehensive medical, dental, and vision coverage
• Flexible Spending Account - healthcare and dependent care
• Health Savings Account - high deductible medical plan
• Retirement 401(k) with employer match
• Paid time off and holidays
• Paid parental leave plans for all new parents
• Leave benefits including disability, paid family medical leave, and paid military leave
• Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees