Data Engineer

McAfee is looking for a Data Engineer to design and optimize scalable data pipelines and analytical datasets that power analytics, machine learning, and AI use cases. This hybrid role is based in Frisco, TX, with an expected path to expanding from core data engineering into data science, statistical analysis, machine learning, and predictive modeling.

• Design, build, optimize, and maintain scalable data pipelines and analytical datasets for analytics, machine learning, and AI initiatives.
• Create reliable data ingestion, transformation, and feature pipelines across large-scale product telemetry, customer, behavioral, feedback, and operational datasets.
• Drive data quality, observability, reliability, performance, and governance practices so datasets remain trusted and analysis-ready.
• Use Python
  , SQL
  , Spark
  , and modern cloud data technologies to process, transform, and analyze large-scale datasets.
• Analyze telemetry and related data sources (usage, behavior, customer feedback, support interactions, and operations) to spot trends, patterns, anomalies, friction points, and opportunities.
• Develop features, metrics, cohorts, and customer segments to deepen understanding of customer behavior and product performance.
• Progressively build statistical and machine learning models focused on customer behavior, product usage, engagement, friction, and customer outcomes.
• Apply methods such as regression, classification, clustering, segmentation, anomaly detection, time-series analysis, forecasting, and predictive modeling aligned to each problem.
• Develop and test hypotheses using statistical analysis and experimentation to clarify drivers of customer and product outcomes.
• Build predictive approaches to identify emerging issues and signals (behavioral changes, product friction, performance concerns) before they broaden.
• Use NLP, embeddings, LLMs, and Generative AI where appropriate to extract insights from unstructured customer signals like feedback, surveys, reviews, and support interactions.
• Evaluate analytical and machine learning models with appropriate statistical and ML techniques to ensure conclusions are supported by the data.
• Partner with Engineering and Product to translate ambiguous questions into measurable data problems, analytical hypotheses, and actionable recommendations.
• Move beyond dashboards by translating data into insights, predictions, recommendations, and measurable product outcomes.
• Operationalize successful models against production datasets, including monitoring and retraining where needed.
• Measure whether insights and downstream product or engineering changes achieve expected impact, creating a continuous learning loop from data to actions to outcomes.

• 5+ years of hands-on experience in Data Engineering, including building and supporting scalable data pipelines, transformations, and analytical datasets.
• Advanced proficiency in Python and SQL
  , with experience working with large-scale structured and unstructured datasets.
• Experience with data processing technologies such as Apache Spark and modern ETL/ELT frameworks.
• Experience with cloud data platforms and services in environments including AWS
  , Azure
  , or GCP
  .
• Strong understanding of data modeling, data quality, data transformations, performance optimization, and building reliable analytical datasets.
• Strong analytical and problem-solving mindset, with demonstrated interest in progressing from data engineering into Data Science, Machine Learning, and advanced analytics.
• Working knowledge of statistics and probability, including hypothesis testing plus regression, classification, clustering, and predictive modeling.
• Hands-on exposure to ML and data science libraries such as pandas
  , Num Py
  , scikit-learn
  , XGBoost
  , Tensor Flow
  , or Py Torch .
• Ability to convert loosely defined product or customer questions into measurable analytical problems and communicate findings clearly to technical and non-technical stakeholders.

• Exposure to behavioral analytics, anomaly detection, segmentation, forecasting, predictive analytics, or other applied data science techniques.
• Familiarity with NLP,
  LLMs
  , embeddings, vector databases,
  RAG
  , and Generative AI for extracting insights from structured and unstructured datasets.
• Coursework, certification, specialization, or a degree in Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related field.
• Motivation to rapidly deepen Data Science and Machine Learning expertise…