Mcafee ML Data Engineer Feature Pipeline & ETL 

Canada 

70019724

The ideal candidate will also have experience supporting the end-to-end ML lifecycle, including model training and experiment tracking, with MLflow experience as a strong asset. As part of our AI and Machine Learning team, you will be instrumental in enabling advanced analytics and delivering personalized user experiences.

• Feature Engineering & Data Integration: Develop and maintain end-to-end ML feature engineering pipelines using Databricks, ensuring data is consistently structured to support ML models effectively.
• Pipeline Development & Management: Integrate diverse data sources (clickstreams, user behaviour, demographic data, etc.) and tailor data integration processes to optimize data quality and performance.
• Medallion Architecture Expertise: Build ETL/ELT pipelines that follow the bronze, silver, and gold layers of the medallion architecture, ensuring efficient data structuring for ML workflows.
• Model Training & Experiment Tracking: Support ML model training and calibration through optimized data pipelines, using MLflow for experiment tracking, model versioning, and performance monitoring.
• Query Optimization & Low Latency Pipelines: Design and implement optimized queries and low-latency data pipelines to support real-time and batch model inference in production.
• CI/CD & Deployment: Apply CI/CD best practices to ensure smooth and efficient pipeline deployments, with automated testing for consistent performance.
• Data Governance & Compliance: Ensure pipelines meet security and compliance standards, particularly for PII, and manage metadata and master data across the data catalogue.
• Collaboration: Work closely with data scientists, data stewards, and other teams to align data ingestion and transformation efforts with business requirements.

About you:

• Experience: Minimum 4 years in data engineering, focusing on ML feature engineering, ETL pipeline development, and data preparation for machine learning.
• Databricks & Medallion Architecture: Proven expertise in managing ETL/ELT pipelines on Databricks, with a solid understanding of the medallion architecture.
• ML Lifecycle & MLflow: Familiarity with the ML lifecycle and experience using MLflow for model training, calibration, and experiment tracking is highly desirable.
• Spark & Big Data Technologies: Advanced skills in Apache Spark for big data processing and analytics.
• Programming & Querying: Strong skills in Python for data manipulation, SQL for query optimization, and performance tuning.
• Low Latency Data Pipelines: Experience in building and optimizing pipelines for low-latency model inference and serving in production environments.
• CI/CD & System Integration: Familiarity with continuous integration and deployment practices for ETL/ELT pipeline development.
• Data Pipeline Management: Expertise in managing data pipelines, ensuring adherence to security, compliance, and best practices.
• Metadata & Master Data Management: Competency in managing metadata and master data within a technical data catalogue
• You are a detail-oriented ML Data Engineer passionate about building scalable, efficient data pipelines tailored for machine learning.
• You thrive in a collaborative environment, working effectively with cross-functional teams to drive data-driven insights and personalized solutions.
• You are proactive in troubleshooting, monitoring, and optimizing data pipelines to support high-performance ML models in production.

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
• Pension and Retirement Plans
• Medical, Dental and Vision Coverage
• Paid Time Off
• Paid Parental Leave
• Support for Community Involvement