Data Engineer

McAfee is hiring a Data Engineer to build and optimize scalable data pipelines and analytical datasets for analytics, machine learning, and AI use cases in Frisco, TX.

Responsibilities

• Design, build, optimize, and maintain scalable data pipelines and analytical datasets for analytics, machine learning, and AI use cases.
• Create reliable 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 so datasets are trusted for analysis and modeling.
• Use Python, SQL, Spark, and modern cloud data technologies to process, transform, analyze, and derive insights.
• Analyze telemetry, usage, behavioral data, customer feedback, support signals, and operational datasets to identify trends, patterns, anomalies, friction points, and opportunities.
• Develop features, metrics, cohorts, and customer segments to better understand customer behavior and product performance.
• Progressively develop statistical and machine learning models to understand customer behavior, product usage, engagement, friction, and outcomes.
• Apply regression, classification, clustering, segmentation, anomaly detection, time-series analysis, forecasting, and predictive modeling based on the problem.
• Develop and test hypotheses using statistical analysis and experimentation to determine drivers of customer and product outcomes.
• Build predictive approaches to identify emerging customer issues, behavioral changes, product friction, and performance problems before they scale.
• 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 ML models with appropriate statistical and ML techniques and ensure conclusions are supported by the data.
• Partner with Engineering and Product to convert 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 model monitoring and retraining where appropriate.
• Measure impact of insights and resulting product or engineering changes, building a continuous learning loop between data, actions, and customer outcomes.

Requirements

• 5+ years hands-on experience in Data Engineering 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 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 interest in progressing from Data Engineering into Data Science, Machine Learning, and advanced analytics.
• Working knowledge of statistics and probability, hypothesis testing, regression, classification, clustering, and predictive modeling.
• Hands-on exposure to ML/DS libraries including pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Ability to move from raw data hypothesis analysis and model conclusion into measurable outcomes.
• 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 is beneficial.
• Coursework, certification, specialization, or a degree in Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, or 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 technical and non-technical stakeholders.

Technologies

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

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

Position Details

• Hybrid role based in Frisco, TX.
• Considering candidates within commutable distance to the Frisco office.
• Required to be onsite on an as-needed basis; work from home office when not onsite.
• No relocation assistance.

Pay Range

• USD $93,370 - USD $153,400 per year depending on experience and qualifications.