Senior Data Engineer

McAfee•Frisco, TX

10h•Hybrid

About The Position

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 based in Texas. We are only considering candidates who are currently in Texas and are not offering relocation.

Requirements

5+ years of hands-on experience in developing ETL/ELT pipelines across varied data sources, with demonstrated ability to work across the full spectrum of data engineering challenges
Experience with Copilot and Claude Anthropic models to enhance development speed, code quality, and documentation
Strong programming skills in languages such as Python, Scala, or Java, with ability to write production-quality code
Experience with modern data platforms and tools (e.g., Snowflake, Databricks, Apache Spark, Kafka, Airflow)
Practical knowledge of Model Context Protocol (MCP) to connect enterprise applications and automate data workflows
Experience with cloud platforms (AWS, Azure, GCP) and their native data services
Knowledge of containerization and orchestration technologies (Docker, Kubernetes)
Strong expertise in data integration, transformation, and curation with a focus on quality and consistency
Experience with real-time data processing and streaming architectures
Background in data science or analytics, with ability to collaborate effectively with data scientists
Experience in client-facing or Professional Services roles
Familiarity with DataOps and MLOps practices
A mindset focused on operational efficiency, automation, and continuous improvement
Strong business acumen with ability to translate technical capabilities into business value
Commitment to SDLC best practices and structured development processes
Excellent communication and collaboration skills, with ability to work effectively with both technical and non-technical stakeholders
Proactive approach to problem-solving with strong analytical and critical thinking skills
Passion for innovation and staying current with emerging technologies and industry trends
Proven experience with both structured and unstructured data, including design and implementation of solutions that leverage both traditional databases and modern cloud architectures
Experience managing sensitive data, including PII, with attention to compliance and governance requirements
Demonstrated ability to work with artificial intelligence, machine learning, and big-data techniques
Solid understanding of data modeling, data warehousing concepts, and dimensional modeling

Responsibilities

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 defend data structures and integrity on an automated basis, implementing proactive monitoring and alerting systems
Troubleshoot pipeline issues and collaborate with platform teams to optimize performance and recovery strategies
Participate in on-call rotations to ensure 24/7 reliability of critical data systems
Continuously evaluate and implement new technologies and methodologies to improve data engineering capabilities
Mentor junior team members and contribute to the growth of the data engineering practice