Data Engineer

McAfee • Frisco, TX

13h • Hybrid

About The Position

We are looking for a Data Engineer with an analytical and Data Science mindset to transform large-scale product, customer, telemetry, behavioral, and operational data into trusted datasets, actionable insights, and predictive intelligence. The role initially focuses on Data Engineering—building, optimizing, and maintaining scalable data pipelines, transformations, and analytical datasets—and is expected to expand into Data Science, statistical analysis, machine learning, and predictive modeling.

This is a hybrid position based in Frisco, Texas. Candidates must live within a commutable distance of the Frisco office. Onsite work is required as needed; otherwise, the employee works from a home office. Relocation assistance is not offered.

The role supports analytics, machine learning, and AI use cases by developing data ingestion, transformation, and feature pipelines, and by establishing practices for data quality, observability, reliability, performance, and governance. It also involves analyzing product and customer data, developing features and metrics, conducting statistical analysis and experimentation, and building models and predictive approaches. The engineer will work closely with Engineering and Product teams to turn customer and product questions into measurable problems and actionable recommendations, and to assess the impact of resulting changes. Successful models may be operationalized against production datasets, with monitoring and retraining where appropriate.

McAfee is a consumer personal-security company focused on helping people live securely through integrated solutions. The company describes its workplace as committed to diversity and inclusion and offers social programs, flexible work hours, and family-friendly benefits.

Requirements

• At least 5 years of hands-on Data Engineering experience, including building and supporting scalable data pipelines, transformations, and analytical datasets.
• Advanced proficiency in Python and SQL and experience 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 reliable analytical datasets.
• Strong analytical and problem-solving mindset and demonstrated interest in 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 to hypothesis, analysis or model, conclusion, recommendation, and measurable outcome.
• Ability to convert loosely defined product or customer questions into measurable analytical problems and communicate findings clearly to technical and non-technical stakeholders.
• Ability and motivation to rapidly develop deeper Data Science and Machine Learning expertise while continuing to apply strong Data Engineering fundamentals.

Nice To Haves

• 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 discipline.

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 product telemetry, customer, behavioral, feedback, and operational datasets.
• Establish practices for data quality, observability, reliability, performance, and governance.
• Use Python, SQL, Spark, and modern cloud data technologies to process, transform, and analyze large-scale datasets and derive insights.
• Analyze product telemetry, usage, behavioral, customer feedback, support, and operational data to identify trends, patterns, anomalies, friction points, and opportunities.
• Develop features, metrics, cohorts, and customer segments to understand customer behavior and product performance.
• 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 as appropriate.
• Develop and test hypotheses through statistical analysis and experimentation to understand factors driving customer and product outcomes.
• Build predictive approaches to identify emerging customer issues, behavioral changes, product friction, performance issues, and other signals.
• Apply NLP, embeddings, LLMs, and Generative AI where appropriate to extract insights from unstructured customer signals.
• Evaluate analytical and machine learning models using appropriate statistical and ML techniques and ensure conclusions are supported by the data.
• Partner with Engineering and Product teams to turn ambiguous customer and product questions into measurable data problems, analytical hypotheses, and actionable recommendations.
• Translate data into insights, predictions, recommendations, and measurable product outcomes beyond dashboards and descriptive reporting.
• Develop scalable approaches for operationalizing successful models against production datasets, including monitoring and retraining where appropriate.
• Measure whether insights and resulting product or engineering changes produce expected impact and support a continuous learning loop.

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
• Social programs, flexible work hours, and family-friendly benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed