United States
Anduril Industries
McAfee
Lead AI Data Engineer - Frisco
United States
Frisco, Texas, United States
PythonSQLAWS
Role Overview
Join our eCommerce Operational Intelligence team as a hands-on Lead AI Data Engineer. You will build enterprise-scale data pipelines and analytics foundations (SQL, Spark/PySpark, ETL/ELT) that produce reliable operational insights and measurable business impact.
The Lead AI 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.
Job Description
About the Role
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.
#LI-Hybrid
Accommodations
McAfee recognizes and supports its obligation to reasonably accommodate applicants and employees with disabilities. We are here to help. Please let us know if you need a reasonable accommodation for any part of the application, interviewing, hiring, or at any other time during the employment process. Please do not include personal medical information in the email.
Diversity is foundational for our business success. We want to be a workplace of choice for all people and we value the unique perspectives offered by a diverse workforce. McAfee does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, citizenship, disability, protected veteran status, age, ancestry, medical condition, genetic information, marital status, pregnancy, or any other legally protected status. This principle applies to all areas of employment: recruitment and hiring, training, performance evaluations, promotions and transfers, compensation and benefits, and social and recreational programs.
McAfee desires to be an employer of choice with an inclusive environment for all individuals. As part of this goal and in compliance with various laws and regulations, McAfee provides reasonable accommodation to applicants and employees. Requests for reasonable accommodation for applicants and employees are evaluated on a case-by-case basis.
Posting Statement
McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.
- Build and operate production ETL/ELT pipelines processing millions of eCommerce events daily and order trends.
- Write and tune complex SQL for operational analytics, KPIs, and reporting.
- Design analytics-ready schemas and data models for performance and scale.
- Troubleshoot pipelines, microservices, and APIs; apply observability to isolate root causes.
- Integrate data across eCommerce, MarTech, and operational systems into unified insights.
- Build AI-driven anomaly detection/notification.
Minimum requirements
About You
Experience
- 10+ years building and architecting large-scale applications and distributed systems.
- 5+ years building production data pipelines, ETL/ELT workflows, and analytics platforms.
- Applied AI to operational intelligence (anomaly detection/alerting, forecasting, insights).
Core Skills (must have)
- Expert SQL (complex queries and performance tuning on large datasets).
- Spark/PySpark in production (Spark SQL, optimization).
- Strong Python (testing, packaging, modern development practices).
- ETL/ELT design: orchestration, scheduling, error handling, monitoring.
- Databricks/Delta Lake (Jobs/Workflows, Unity Catalog; Medallion patterns).
- Data modeling for analytics (dimensional models; star/snowflake schemas).
- Distributed systems fundamentals: microservices, APIs, event-driven patterns.
- Production troubleshooting and observability (logs, metrics, traces, alerting).
- AWS (S3, Lambda, Glue, Kinesis, OpenSearch, QuickSight, CloudWatch); BI (Power BI, Tableau, Grafana); LLM/GenAI (RAG, vector DBs, LangChain, Bedrock).
Application Development & System Understanding
- Distributed systems engineering (microservices, APIs, event-driven architecture, scalability patterns).
- Strong engineering discipline (design patterns, testing, code quality, Git, CI/CD) with rapid system comprehension.
- Production troubleshooting, observability (root-cause analysis; logs/metrics/traces/alerting) and AI agents for automated monitoring/notification.
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