Data Engineer

McAfee is hiring a Data Engineer (hybrid) in Frisco, TX to build scalable data pipelines and analytical datasets that enable analytics, machine learning, and AI use cases.

Responsibilities

• Design, build, optimize, and maintain scalable data pipelines and analytical datasets for analytics, machine learning, and AI.
• Create 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 so datasets are trusted for analysis and modeling.
• Use Python, SQL, Spark, and modern cloud data technologies to process, transform, analyze, and derive insights from large datasets.
• Analyze product telemetry, usage, behavioral data, customer feedback, support, and operational datasets to uncover 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 covering 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.
• Develop and test hypotheses using statistical analysis and experimentation to identify factors driving customer and product outcomes.
• Build predictive approaches to identify emerging customer issues, behavioral changes, product friction, and performance signals before they broaden.
• Use NLP, embeddings, LLMs, and Generative AI where appropriate to extract insights from unstructured customer signals (feedback, surveys, reviews, support interactions).
• Evaluate analytical and machine learning models with appropriate statistical and ML techniques to ensure conclusions align with the underlying data.
• Collaborate with Engineering and Product to convert ambiguous customer and product questions into measurable data problems, analytical hypotheses, and actionable recommendations.
• Move beyond dashboards by turning data into insights, predictions, recommendations, and measurable product outcomes.
• Operationalize successful models against production datasets, including model monitoring and retraining as appropriate.
• Measure the impact of insights and related product or engineering changes, maintaining a continuous learning loop between data, actions, and customer outcomes.

Requirements

• 5+ years 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 such as AWS, Azure, or GCP.
• Strong understanding of data modeling, data quality, data transformations, performance optimization, and reliable analytical dataset creation.
• Analytical and problem-solving mindset with interest in expanding from data engineering into Data Science, Machine Learning, and advanced analytics.
• Working knowledge of statistics and probability, including hypothesis testing, regression, classification, clustering, and predictive modeling.
• Hands-on experience with 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 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 grow 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 both 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.
• Only considering candidates within a commutable distance to the Frisco office.
• Onsite required on an as-needed basis; work from your home office when not onsite.
• No relocation assistance.

Pay Range

• USD $93,370.00 per year to USD $153,400.00 per year, depending on experience and qualifications.