Sr Data Engineer

McAfee is seeking a Sr Data Engineer to design and enhance scalable data pipelines, transformations, and analytical datasets. This hybrid role in Frisco, TX starts with strong Data Engineering responsibilities and is intended to expand into data science, machine learning, and predictive modeling to support product, customer, telemetry, behavioral, and operational use cases.

Responsibilities

• Design, build, optimize, and maintain scalable data pipelines and analytical datasets for analytics, machine learning, and AI use cases.
• Build reliable data ingestion, transformation, and feature pipelines across large-scale product telemetry, customer, behavioral, feedback, and operational datasets.
• Implement data quality, observability, reliability, performance, and governance practices to ensure datasets are trustworthy and analysis-ready.
• Use Python, SQL, Spark, and modern cloud data technologies to process, transform, analyze, and derive insights from large-scale datasets.
• Analyze telemetry, usage, behavioral signals, customer feedback, support interactions, and operational data to identify trends, patterns, anomalies, friction points, and opportunities.
• Create features, metrics, cohorts, and customer segments to support deeper understanding of customer behavior and product performance.
• Progressively develop statistical and machine learning models to understand customer behavior, product usage, engagement, friction, and customer outcomes.
• Apply regression, classification, clustering, segmentation, anomaly detection, time-series analysis, forecasting, and predictive modeling aligned to the problem being solved.
• Develop and test hypotheses using statistical analysis and experimentation to identify factors influencing product and customer outcomes.
• Build predictive approaches that detect emerging issues, behavioral changes, product friction, performance concerns, and other early signals.
• Use NLP, embeddings, LLMs, and Generative AI when appropriate to extract insights from unstructured customer signals such as feedback, surveys, reviews, and support interactions.
• Evaluate analytical and machine learning models using suitable statistical and ML techniques and ensure conclusions are supported by the underlying data.
• Partner with Engineering and Product teams to translate ambiguous questions into measurable data problems, analytical hypotheses, and actionable recommendations.
• Move from dashboards and descriptive reporting toward insights, predictions, recommendations, and measurable product outcomes.
• Operationalize models using production datasets, including model monitoring and retraining where appropriate.
• Measure impact by verifying whether insights and downstream product or engineering changes deliver expected results, maintaining a continuous learning loop between data, actions, and customer outcomes.

Requirements

• 10+ 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 such as AWS, Azure, or GCP.
• Strong understanding of data modeling, data quality, data transformations, performance optimization, and creation of reliable analytical datasets.
• Strong analytical and problem-solving mindset, including a demonstrated interest in moving beyond Data Engineering into Data Science, Machine Learning, and advanced analytics.
• Working knowledge of statistics and predictive modeling topics including hypothesis testing, regression, classification, clustering, and related approaches.
• Hands-on exposure to Data Science and ML libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Exposure to behavioral analytics, anomaly detection, segmentation, forecasting, predictive analytics, or similar applied techniques is highly desirable.
• Familiarity with NLP, LLMs, embeddings, vector databases, RAG, and Generative AI for extracting insights from structured and unstructured datasets is beneficial.
• Coursework, certification, specialization, or a degree in a related discipline such as Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, or equivalent is preferred.
• Demonstrated ability and motivation to rapidly deepen Data Science and Machine Learning expertise while continuing to apply strong Data Engineering fundamentals.
• Ability to translate loosely defined product or customer questions into measurable analytical problems and communicate findings clearly to technical and non-technical stakeholders.

Technologies

• Python, SQL, Spark (Apache Spark)
• AWS, Azure, GCP
• ETL/ELT
• pandas, NumPy
• scikit-learn, XGBoost
• TensorFlow, PyTorch
• NLP, embeddings, LLMs
• Generative AI, vector databases, RAG
• time-series analysis

Benefits

• Bonus Program
• 401k Retirement
• Medical, Dental, Vision, Basic Life, Short Term Disability and Long-Term Disability Coverage
• Paid Parental Leave
• Support and Community Involvement
• 14 Paid Company Holidays
• Unlimited Paid Time Off for Exempt Employees
• 96 Hours of Sick Time and 120 Hours of Vacation for Non-Exempt Employees Accrued Each Year

Role and Position Details

• Hybrid role based in Frisco, TX.
• McAfee is only considering candidates within a commutable distance to the Frisco office.
• Onsite presence is required on an as-needed basis; otherwise work is performed from a home office.
• No relocation assistance is offered.

Company Benefits and Perks

• McAfee supports diversity and inclusion and encourages employees to bring their authentic selves to work.
• Social programs, flexible work hours, and family-friendly benefits are provided to employees.