דרושים אנליסט מידע ב-Mcafee ב-קנדה
Mcafee Senior Data Architect Canada, Ontario, Waterloo
- Data lakehouse modeling and design
- ETL/ELT design
- Dimensional and relational data modeling
- Data warehouse design
- Security and PII management
andorganize data platforms, including databases and data structures, data storage, access, loading, and pipeline design.
Collaborate with data stewards and data engineers on solution design.
Develop andmaintainphysical data models for the Databricks platform.
Managemetadata in data catalog tools, such as Collibra.
Ensure adherence to data curation standards and guidelinesfor medallion certification framework and required architecture.
Ensure the platform and platform design meets required security and compliance guidelines including PII management.
Provide oversight todatacuration teams,ingestion engineersand data engineers, including code review and data model design review to ensure adherence to standards and best practices.
6+ years of experience in data engineering, data modeling, and ETL/ELT.
Have experienceas a senior engineer, including conducting code reviews.
Have hands-on experience withsix or more of the following:
Design and implementation of logical and physical data models.
Data modeling tools, such as Erwin or ER/Studio.
Databricks /Spark.
Lakehouse architectureand medallion data organization.
ETL/ELT development, monitoring,pipeliningand orchestration.
Python/PySpark, Scala or similar programming language.
Data quality guidelinesand testing frameworks.
Creating technical design documents and data flow diagrams.
CI/CD pipelines.
Agile sprints.
Git and version control.
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
משרות נוספות שיכולות לעניין אותך
Mcafee Data Scientist Product Canada
About the role:
- Develop and train optimal ML models in big data environment for use in product features and with the purpose of maximizing business & product outcomes
- Curate and manage telemetry, dataflows and workflow automation to operationalize and systemize creation and use of up-to-date ML models in a continuous improvement strategy
- As an embedded and integral part of a product team, perform the duties and rituals of an agile development lifecycle
- Work with team and cross-team members to design, develop, debug and release production quality products
About you:
- 2 to 5 years of proven track record of ML and deep learning industry experience
- Excellent oral, written, and interpersonal skills
- Advanced degree in Data Science, Statistics, Computer Science or a related field
- Expertise in data science lifecycle and data modeling
- Proficiency in SQL, Python and/or R
- Using databases and big data systems
- General programming skills a plus, including C/C++, JS and other common languages
- Collaborating, analyzing, documenting and converging on software design and data modeling in a large group
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
Mcafee ML Data Engineer Feature Pipeline & ETL Canada, Ontario, Waterloo
About the role:
- 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.
- 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
- Data lakehouse modeling and design
- ETL/ELT design
- Dimensional and relational data modeling
- Data warehouse design
- Security and PII management
andorganize data platforms, including databases and data structures, data storage, access, loading, and pipeline design.
Collaborate with data stewards and data engineers on solution design.
Develop andmaintainphysical data models for the Databricks platform.
Managemetadata in data catalog tools, such as Collibra.
Ensure adherence to data curation standards and guidelinesfor medallion certification framework and required architecture.
Ensure the platform and platform design meets required security and compliance guidelines including PII management.
Provide oversight todatacuration teams,ingestion engineersand data engineers, including code review and data model design review to ensure adherence to standards and best practices.
6+ years of experience in data engineering, data modeling, and ETL/ELT.
Have experienceas a senior engineer, including conducting code reviews.
Have hands-on experience withsix or more of the following:
Design and implementation of logical and physical data models.
Data modeling tools, such as Erwin or ER/Studio.
Databricks /Spark.
Lakehouse architectureand medallion data organization.
ETL/ELT development, monitoring,pipeliningand orchestration.
Python/PySpark, Scala or similar programming language.
Data quality guidelinesand testing frameworks.
Creating technical design documents and data flow diagrams.
CI/CD pipelines.
Agile sprints.
Git and version control.
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
משרות נוספות שיכולות לעניין אותך
