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# Sr. Data Engineer

- **Company:** McAfee, Inc.
- **Location:** Frisco, TX, United States
- **Experience:** Expert
- **Salary:** $135,910.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Data Analysis, Software Applications, Big Data, Cloud Computing, Computer Programming, Information Engineering, Extract Transform Load (ETL), Data Systems, Data Warehousing, Distributed Data Store, Distributed Systems, Amazon DynamoDB, Apache Hadoop, Python (Programming Language), Machine Learning, Microsoft SQL Server, MongoDB, NoSQL, RabbitMQ, Redis, Search Technologies, SQL Databases, Systems Integration, Technical Data Management Systems, Data Processing, Cloud Platform System, Data Ingestion, Spring Cloud, Large Language Models, Prompt Engineering, Apache Spark, Generative AI, Event Driven Architecture, Kubernetes, Cassandra, AWS Fargate, Apache Kafka, Data Management, Stream Processing, Data Pipelines, Docker, Golang, Microservices
- **Published:** September 25, 2026
- **Apply:** https://www.careerjet.com/job/usf646a96b95d78ae63ce5a9e6d64db3f3/eaa

## About the Role

* 10+ years of software and data engineering experience, including significant experience building large-scale cloud-based data platforms.
 * Strong analytical mindset with demonstrated ability to move beyond data processing and derive meaningful insights from complex datasets.

 * Experience translating telemetry, behavioral, operational, and product data into actionable recommendations for Product and Engineering teams.

 * Ability to connect technical data signals with customer experience and business outcomes, and communicate those insights effectively to technical and non-technical stakeholders.

 * Strong programming experience with Python and Golang, including building distributed systems, APIs, and cloud-native applications.

 * Deep expertise in SQL, NoSQL, and large-scale data processing technologies including DynamoDB, MS SQL, Cassandra, MongoDB, Apache Spark, Hadoop, and S3.

 * Hands-on experience designing and supporting real-time data processing and event-driven architectures using Kafka, Redis, RabbitMQ, or similar technologies.

 * Experience designing and optimizing ETL/ELT pipelines, data warehouses, and enterprise-scale data platforms.

 * Knowledge of containerization and orchestration technologies including Docker, Kubernetes, EKS, and Fargate.

 * Experience supporting AI/ML workloads, including data pipelines for machine learning, LLM integrations, prompt engineering, vector databases, and RAG architectures.

 * Demonstrated ability to lead complex technical initiatives and influence engineering decisions across multiple teams.

 * Strong communication and collaboration skills with experience partnering across Engineering, Product, Data Science, and business stakeholders.

## Description

* Design, build, and maintain scalable data platforms, pipelines, and services that support analytics, machine learning, and AI-powered applications.

 * Architect and optimize batch and real-time data processing solutions across cloud-native environments, including AWS and GCP.

 * Analyze large-scale product, customer, and operational datasets to identify trends, patterns, anomalies, and opportunities that translate into meaningful product and engineering insights.

 * Turn data into actionable recommendations that improve product experience, customer satisfaction, engagement, performance, reliability, and business outcomes.

 * Partner with Product, Engineering, Data Science, and UX teams to define meaningful metrics, investigate customer behavior and friction points, and influence product priorities using data.

 * Leverage AI/ML and advanced analytics to uncover insights at scale, identify emerging patterns, predict potential issues, and accelerate root-cause analysis

 * Develop high-performance data APIs, microservices, and integrations using Python, Golang, and modern containerized architectures.

 * Lead the design and implementation of distributed data systems utilizing technologies such as Spark, Hadoop, Kafka, DynamoDB, MongoDB, Cassandra, and S3.

 * Build and maintain reliable data ingestion, transformation, and orchestration frameworks that enable trusted, high-quality data across the organization.

 * Design and implement streaming and event-driven architectures using technologies such as Kafka, Redis, and RabbitMQ.
 * Develop and optimize data solutions supporting LLM-based applications, retrieval-augmented generation (RAG), vector search, and AI model workflows.

 * Establish engineering standards for data quality, observability, governance, performance, reliability, and security.

 * Lead technical design discussions, conduct architecture reviews, and influence data engineering best practices across multiple teams and initiatives.

 * Mentor engineers and provide technical leadership on complex cross-functional projects.