Senior Data Engineer

Role Overview

As a Data Engineer at McAfee, you will be a key member of our data innovation team, responsible for designing, building, and overseeing the deployment and operation of technology architecture, solutions, and software that unlock the full potential of our data assets. This role combines hands‑on technical implementation with strategic problem‑solving to drive data‑driven innovation across the organization.

You will establish and build processes and structures to capture, manage, store, and utilize structured and unstructured data from diverse internal and external sources, creating scalable solutions that span from cloud‑based architectures to traditional databases. Working at the intersection of data engineering, data science, and data quality, you will leverage artificial intelligence, machine learning, and big‑data techniques to transform raw data into actionable insights that drive business value.

This is a collaborative role where you will partner closely with business stakeholders, data scientists and product teams to solve complex problems, enable company‑wide data solutions, and establish the foundation for data‑driven decision making across McAfee.

This is a Hybrid Position located in either Waterloo or Toronto, Canada. We are only considering candidates within a commutable distance to either location. You will be required to be onsite on an as‑needed basis; when not working onsite, you will work from your home office.

• Partner with business stakeholders to understand data requirements and translate them into scalable technical solutions that drive operational efficiency and strategic insights
• Lead data innovation initiatives by identifying opportunities to leverage data assets for new business capabilities and competitive advantages
• Review internal and external business and product requirements for data operations and recommend strategic changes and upgrades to systems and storage
• Collaborate with data scientists to enable advanced analytics, predictive modeling, and machine learning initiatives that solve complex business problems
• Work with Professional Services teams on client‑focused data solutions, ensuring alignment with business objectives and customer needs
• Design and oversee the deployment of comprehensive data architecture that captures, manages, and stores structured and unstructured data from multiple internal and external sources
• Build resilient ETL/ELT pipelines that channel data from multiple inputs, route appropriately, and store using cloud structures, local databases, and other applicable storage forms
• Establish processes and structures based on business and technical requirements to ensure optimal data flow across systems
• Create and maintain well‑documented data services and interfaces for efficient data access across the organization
• Develop company‑wide, web‑enabled solutions that democratize data access and empower self‑service analytics
• Develop technical tools and programming leveraging artificial intelligence, machine learning, and big‑data techniques to cleanse, organize, and transform data on an automated basis
• Implement comprehensive data quality frameworks including validation checks, monitoring, and automated recovery strategies to maintain data accuracy, completeness, and freshness
• Apply business logic to cleanse, enrich, and structure raw data, ensuring consistency and quality across domains
• Leverage Model Context Protocol (MCP) to connect with top enterprise applications, enabling seamless automation of data flows and improving operational efficiency
• Utilize Copilot and Anthropic models to accelerate development, automate documentation, and enhance code quality and review processes
• Create and establish design standards and assurance processes for software, systems, and applications development to ensure compatibility and operability of data connections, flows, and storage requirements
• Ensure secure, scalable, and auditable data ingestion processes, with appropriate handling of PII and compliance requirements
• Uphold SDLC best practices across development and delivery stages to ensure reliability, maintainability, and scalability
• Maintain and…