Principal Engineer - Conversational Analytics (Hybrid - Seattle, WA)

Nordstrom•Seattle, WA

2d•$162,000 - $252,500•Hybrid

About The Position

Come join us in a key technical leadership role revolutionizing how Nordstrom unlocks insights from conversational data! We are building cutting-edge conversational analytics capabilities within our Enterprise Data Platform (EDP) to transform how we understand and respond to customer interactions across all touchpoints. We are seeking an experienced Principal Engineer to lead the design and implementation of cutting-edge Conversational Analytics solutions that will transform how Nordstrom business interacts with data and analytics across the enterprise. This role will drive the technical strategy for extracting actionable insights from Enterprise Data Platform and BI Tools, enabling data-driven decisions that enhance customer experience and business outcomes. A Day in the Life… Architect conversational analytics solutions that can process and analyze datasets in our Data Platform (Big Query) and BI tooling at scale. Lead the technical vision for conversational AI and natural language processing capabilities within the Enterprise Data Platform, ensuring solutions are scalable, reliable, and cost-effective. Drive technical excellence across your engineering team, establishing best practices for machine learning operations (MLOps), model deployment, and AI system monitoring. Collaborate with cross-functional teams including data science, product, customer service, and business stakeholders to understand requirements and deliver solutions that drive business impact. Design and implement data pipelines that can ingest, process, and analyze conversational data from multiple sources including customer service transcripts, chat logs, reviews, and social media. Mentor and develop senior engineers, providing technical guidance and helping them grow their expertise in AI, machine learning, and conversational analytics. Drive innovation by staying current with the latest advances in conversational AI, large language models, and natural language processing technologies. Ensure data quality and governance for conversational data, implementing proper data security, privacy, and compliance measures. Lead architectural discussions and make key technical decisions that will shape the future of conversational analytics at Nordstrom. Partner with platform teams to integrate conversational analytics capabilities seamlessly into the broader Enterprise Data Platform ecosystem.

Requirements

8+ years of relevant work experience in software engineering with significant focus on data engineering, machine learning, or AI systems.
Bachelor's degree in Computer Science, Data Science, or a related field , with a master's degree preferred, or equivalent experience.
3+ years of technical leadership experience leading engineering teams in dynamic, high-scale environments.
Deep expertise in conversational AI and NLP , including experience with large language models, natural language understanding, sentiment analysis, and intent detection.
Strong background in machine learning engineering , including model training, deployment, monitoring, and MLOps best practices.
Experience with cloud platforms (preferably Google Cloud Platform) and big data technologies for processing conversational data at scale.
Proven track record of building production-grade AI/ML systems that handle real-time data processing and serve business-critical insights.
Experience with streaming data architectures and event-driven systems, particularly for processing unstructured text data.
Strong programming skills in languages commonly used for ML engineering (Python, SQL, etc.) and familiarity with ML frameworks and libraries.
Customer-focused mindset with ability to translate business requirements into technical solutions and communicate complex technical concepts to non-technical stakeholders.
Experience with data governance, privacy, and security especially as it relates to customer conversation data and AI model compliance.
Demonstrated ability to drive technical innovation while balancing practical business needs and engineering constraints.

Nice To Haves

Experience with retail or e-commerce conversational analytics
Background in customer experience analytics and sentiment analysis
Experience with real-time conversation analysis and chatbot technologies
Knowledge of privacy-preserving ML techniques for sensitive customer data
Previous experience scaling conversational AI solutions across multiple business units

Responsibilities

Architect conversational analytics solutions that can process and analyze datasets in our Data Platform (Big Query) and BI tooling at scale.
Lead the technical vision for conversational AI and natural language processing capabilities within the Enterprise Data Platform, ensuring solutions are scalable, reliable, and cost-effective.
Drive technical excellence across your engineering team, establishing best practices for machine learning operations (MLOps), model deployment, and AI system monitoring.
Collaborate with cross-functional teams including data science, product, customer service, and business stakeholders to understand requirements and deliver solutions that drive business impact.
Design and implement data pipelines that can ingest, process, and analyze conversational data from multiple sources including customer service transcripts, chat logs, reviews, and social media.
Mentor and develop senior engineers, providing technical guidance and helping them grow their expertise in AI, machine learning, and conversational analytics.
Drive innovation by staying current with the latest advances in conversational AI, large language models, and natural language processing technologies.
Ensure data quality and governance for conversational data, implementing proper data security, privacy, and compliance measures.
Lead architectural discussions and make key technical decisions that will shape the future of conversational analytics at Nordstrom.
Partner with platform teams to integrate conversational analytics capabilities seamlessly into the broader Enterprise Data Platform ecosystem.