Lead AI Enterprise Data Engineer – eCommerce

McAfee•Frisco, TX

10h•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 at our Frisco, TX. You will be required to be on-site 2 to 3 days a week on a 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

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.
Reliable real-time and historical eCommerce monitoring.
Improved funnel conversion via predictive analytics.
Faster incident triage via AI-driven insights.
Higher engineering productivity via AI copilots.
Cost-efficient AI and data operations across AWS + Databricks.

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.