Sr Data Engineer

Role Summary We are looking for a strong Sr Data Engineer with an analytical and Data Science mindset to help transform large-scale product, customer, telemetry, behavioral, and operational data into trusted datasets, actionable insights, and predictive intelligence. This role will initially focus on Data Engineering, including building, optimizing, and maintaining scalable data pipelines, transformations, and analytical datasets. With our core data pipelines and datasets increasingly established, the role is expected to quickly expand into Data Science, statistical analysis, machine learning, and predictive modeling. This is a Hybrid position based in Frisco, TX. We are only considering candidates within a commutable distance to the Frisco office. You will be required to be onsite on an as-needed basis; when not working onsite, you will work from your home office. We are not offering relocation assistance. Position Details About the Role Design, build, optimize, and maintain scalable data pipelines and analytical datasets supporting 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. Establish strong practices around data quality, observability, reliability, performance, and governance to ensure datasets are trusted and ready for analysis and modeling. Use Python, SQL, Spark, and modern cloud data technologies to process, transform, analyze, and derive insights from large-scale datasets. Analyze product telemetry, usage, behavioral, customer feedback, support, and operational datasets to identify trends, patterns, anomalies, friction points, and opportunities. Develop meaningful features, metrics, cohorts, and customer segments that enable 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 techniques such as regression, classification, clustering, segmentation, anomaly detection, time-series analysis, forecasting, and predictive modeling based on the problem being solved. Develop and test hypotheses using statistical analysis and experimentation to understand the factors driving customer and product outcomes. Build predictive approaches that help identify emerging customer issues, behavioral changes, product friction, performance issues, and other signals before they become broader problems. Apply NLP, embeddings, LLMs, and Generative AI where appropriate to extract insights from unstructured customer signals such as feedback, surveys, reviews, and support interactions. Evaluate analytical and machine learning models using appropriate statistical and ML techniques and ensure conclusions are supported by the underlying data. Partner closely with Engineering and Product teams to translate ambiguous customer and product questions into measurable data problems, analytical hypotheses, and actionable recommendations. Move beyond dashboards and descriptive reporting by translating data into insights, predictions, recommendations, and measurable product outcomes. Develop scalable approaches for operationalizing successful models against production datasets, including model monitoring and retraining where appropriate. Measure whether insights and resulting product or engineering changes produce the expected impact, creating a continuous learning loop between data, insights, actions, and customer outcomes. About You 10+ years hands-on experience in Data Engineering, with experience 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 working with cloud data platforms and services in environments such as 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 a demonstrated interest in moving beyond data engineering into Data Science, Machine Learning, and advanced analytics. Working knowledge of statistics, probability, hypothesis testing, regression, classification, clustering, and predictive modeling. Hands-on exposure to Data Science and ML libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or PyTorch. Ability to move from raw data → hypothesis → analysis/model → conclusion → recommendation → measurable outcome. Exposure to behavioral analytics, anomaly detection, segmentation, forecasting, predictive analytics, or other applied Data Science 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 Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related discipline is preferred. Demonstrated ability and motivation to rapidly develop deeper Data Science and Machine Learning expertise while continuing to apply strong Data Engineering fundamentals. Ability to convert loosely defined product or customer questions into measurable analytical problems and communicate findings clearly to both technical and non-technical stakeholders. #LI-Hybrid Company Overview McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users’ needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment. Company Benefits and Perks We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees.: 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 We're serious about our commitment to diversity which is why 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. Pay Range The anticipated compensation for this position is USD $123,650.00/Yr. - USD $203,150.00/Yr. depending on experience and qualifications. 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