Lead AI Enterprise Data Engineer

McAfee•Frisco, TX

21h•Hybrid

About The Position

The Lead AI Enterprise Data Engineer will drive the transformation of eCommerce operational analytics and real-time monitoring by building scalable data pipelines, AI-powered insights, and intelligent dashboards. This role leads AI proof-of-concepts and contributes to production-grade solutions that improve platform reliability, accelerate root-cause identification, enhance engineering productivity, and strengthen operational intelligence across McAfee’s eCommerce ecosystem. This is a Hybrid position located in Frisco, TX. You will be required to be on-site on an as-needed basis; when you are not working on-site, you will work from your home office. You must be within commutable distance of Frisco, TX. We are not offering relocation assistance at this time. About The Role: Design operational and business metrics using transactional data, telemetry, and customer behavior data. Build near real-time dashboards for eCommerce funnel metrics, subscription flows, payments, and operational KPIs. Develop scalable data pipelines using Databricks (PySpark, Delta Lake, Workflows, Medallion Architecture). Optimize Databricks-based data lake structures for analytics across eCommerce and MarTech domains. Build automated data quality validation frameworks. Build AI models for anomaly detection, churn forecasting, and funnel drop-off prediction. Create AI-powered copilots and tools to boost engineering productivity. Integrate LLMs (OpenAI, Claude, LangChain, OSS models). Build AI agents using AWS GenAI tooling. Track AI-driven KPIs and deploy enterprise/local LLM solutions. Integrate AI systems with eCommerce platform APIs, telemetry, and Databricks workflows. Build orchestration layers for automated decision-making. Architect scalable data + AI solutions using Databricks + AWS. Implement CI/CD for AI/ML workflows. Build cost optimization and AI usage governance tooling. Automate incident intelligence and reporting. About You: 10+ years enterprise application development experience. 3+ years hands-on AI/ML experience. Expert-level Databricks experience (PySpark, Delta Lake, Workflows). Experience with AWS for AI agent building and GenAI integrations. Strong SQL/NoSQL experience. Experience with BI tools (Power BI, Tableau, Looker, Grafana). eCommerce or Martech domain knowledge preferred. #LI-Hybrid

Requirements

10+ years enterprise application development experience.
3+ years hands-on AI/ML experience.
Expert-level Databricks experience (PySpark, Delta Lake, Workflows).
Experience with AWS for AI agent building and GenAI integrations.
Strong SQL/NoSQL experience.
Experience with BI tools (Power BI, Tableau, Looker, Grafana).

Nice To Haves

eCommerce or Martech domain knowledge preferred.

Responsibilities

Design operational and business metrics using transactional data, telemetry, and customer behavior data.
Build near real-time dashboards for eCommerce funnel metrics, subscription flows, payments, and operational KPIs.
Develop scalable data pipelines using Databricks (PySpark, Delta Lake, Workflows, Medallion Architecture).
Optimize Databricks-based data lake structures for analytics across eCommerce and MarTech domains.
Build automated data quality validation frameworks.
Build AI models for anomaly detection, churn forecasting, and funnel drop-off prediction.
Create AI-powered copilots and tools to boost engineering productivity.
Integrate LLMs (OpenAI, Claude, LangChain, OSS models).
Build AI agents using AWS GenAI tooling.
Track AI-driven KPIs and deploy enterprise/local LLM solutions.
Integrate AI systems with eCommerce platform APIs, telemetry, and Databricks workflows.
Build orchestration layers for automated decision-making.
Architect scalable data + AI solutions using Databricks + AWS.
Implement CI/CD for AI/ML workflows.
Build cost optimization and AI usage governance tooling.
Automate incident intelligence and reporting.