About the Role
Partner with business stakeholders to understand data requirements and translate them into scalable technical solutions that drive operational efficiency and strategic insights.
Identify opportunities to leverage LLMs, RAG pipelines, and ML systems for new internal capabilities and automation.
Collaborate with data scientists to enable advanced analytics, predictive modeling, and machine learning initiatives that solve complex business problems.
Provide regular reporting and findings to stakeholders on pipeline health, model performance, and analytical outputs.
Design and build scalable batch and real-time streaming data pipelines across cloud environments (AWS, Google Cloud).
Develop and maintain ETL/ELT pipelines that ingest, transform, and store structured and unstructured data from multiple internal and external sources.
Lead the design and implementation of stream processing architectures for high-throughput, low-latency use cases using Kafka, Apache Beam, or equivalent.
Design and build production-grade data services and APIs, owning the full software lifecycle from design through deployment and operation.
Manage database migrations and ensure optimal data flow and storage across systems.
Create and maintain well-documented data services and interfaces for efficient data access across the organization.
Build internal tooling leveraging LLMs and RAG pipelines to process, analyze, and query large data assets at scale.
Apply ML techniques — including contextual bandits, reinforcement learning, and recommendation systems — to business problems where applicable.
Develop and deploy production ML systems in collaboration with data scientists, taking models from experimentation to operational reliability.
Apply software engineering best practices — testing, CI/CD, code review — to data pipelines and ML systems.
Provision and manage cloud infrastructure using Docker and Terraform to ensure reproducible, scalable deployments.
Implement data quality frameworks including validation checks, monitoring, and automated recovery strategies to maintain data accuracy, completeness, and freshness.
Ensure secure and auditable data ingestion processes with appropriate handling of sensitive data and compliance requirements.
Uphold SDLC best practices across development and delivery stages to ensure reliability, maintainability, and scalability.
Troubleshoot pipeline and model issues; collaborate with platform teams to optimize performance and recovery strategies.
Continuously evaluate and implement new technologies — including emerging LLM and Gen AI tooling — to improve data engineering capabilities.
Mentor junior engineers and contribute to the growth of the data engineering practice.
About You
10+ years of hands-on experience in data engineering, software engineering, or a closely related field.
Strong Python programming skills with production-quality code; Java a plus.
Hands-on experience building batch and real-time streaming pipelines (Kafka, Apache Beam, or equivalent).
Practical experience with LLMs and RAG pipelines in a production or near-production context.
Experience with ML frameworks and techniques: scikit-learn, TensorFlow, contextual bandits, or reinforcement learning.
Cloud platform experience: AWS and/or Google Cloud.
Containerization and infrastructure-as-code: Docker and Terraform.
Strong SQL and data modeling fundamentals.
Track record of owning complex technical problems end-to-end with minimal oversight.
Ability to communicate analytical findings and infrastructure health clearly to non-technical stakeholders.
Deep commitment to code quality, testing, and SDLC best practices.
Proactive problem-solver with strong analytical and critical thinking skills.
Passionate about applied ML and emerging AI technologies.
Strong mentoring and knowledge-sharing capabilities.
Preferred Qualifications include: Experience with Databricks, Snowflake, or Apache Spark, familiarity with Opensearch or vector search technologies, research background or publications in ML/data science, experience with online learning frameworks (e.g. Vowpal Wabbit), familiarity with Model Context Protocol (MCP) or Anthropic/Copilot models.
10+ years of hands-on experience in data engineering, software engineering, or a closely related field is a plus.
#LI-Hybrid
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Company Benefits and Perks:
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.
We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.
The starting pay range for this position is $135,910.00-$223,285.00. McAfee takes into consideration an individual’s skillset, experience and location in making final salary determinations. For further details, please discuss with the Talent Acquisition Partner.Please click here to view and download the Job Applicant Privacy Notice, which applies to all McAfee job applicants who are residents of the state of California.