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

What will you do

• Enhance existing sales and renewals statistical models with AI-assisted contextual reasoningwithout 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 Bedrockpowered 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 enrichnot overridetraditional 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

• Experience: 10 years of experience as a Data Engineer BI Engineer or Systems Analyst in an enterprise environment with large complex data sources.

• Education: Masters degree in Computer Science IT Engineering or equivalent experience.

• Expert SQL: Deep expertise in relational databases (PostgreSQL MSSQL etc.) and query optimization.

• Expert Python: Strong programming skills for data querying cleaning and presentation with hands-on experience in data-centric libraries.

• Modern Stack Experience: Working knowledge of DBT (Data Build Tool) and Snowflake data warehousing is highly desirable.

• ELT/ETL Tools: Experience with Fivetran or similar integration tools.

• Autonomy: Ability to manage multiple projects simultaneously in a fast-paced distributed team environment across different time zones and cultures.

• Troubleshooting: Exceptional logic and reasoning skills to troubleshoot complex data issues.

• Planning: Ability to think strategically about data architecture and project planning.

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