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

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

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