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 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 Apart: You 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?

• 10+ years of experience as an Application Engineer, Data Engineering, Platform Engineer, or Software Engineer working in large-scale enterprise environments, building and maintaining complex, data-intensive applications, distributed systems, and integrations across multiple data sources and services.

• Hands-on experience designing REST/GraphQL APIs for data access, aggregation, and transformation, with strong focus on performance, security, and contract stability.

• Education: Master’s 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.

#Li-NG1