Data Engineer - AI-Enabled Capacity Analytics

Location: Mount Laurel

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love.

We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work.

If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)Job Summary Join Comcast's National Traffic and Capacity Management (NTCM) team as a Data Engineer III and help build trusted data solutions that support network capacity planning and optimization across next-generation products, including EOHFC, EPON vBNG, and FDX.

In this role, you will build and maintain scalable data pipelines, BI and Planning applications, and automated quality check processes that enable forecasting, analysis, and operational decision-making. You will work across data engineering, analytics, and applied AI, with opportunities to support machine learning models, AI-assisted analysis, and workflow automation.

Job Description This position is ineligible for visa sponsorship. To be considered for this role, you must be legally authorized to work in the United States and not require sponsorship for employment now or in the future.

Design, develop, test, deploy, and support scalable data applications, planning tools, APIs, and data pipelines for network capacity planning and analysis. Build and optimize ELT and ETL processes across  batch and streaming datasets. Develop automated data-quality checks, anomaly detection, master data management, metadata management, lineage, and monitoring processes.

Develop, tune, and fine-tune machine learning and AI models; evaluate model performance and integrate models into production data applications and workflows. Develop automation for recurring workflows, including orchestration, validation, monitoring, and issue notification. Provide system documentation. Perform ad hoc data analysis and support AI-assisted insight generation. Optimize and monitor data-intensive processes and applications; identify and resolve performance, scalability, and data-quality issues.

Create and maintain clear documentation for data pipelines, software specifications, system designs, processes, and support procedures. Apply sound practices for versioning, testing, privacy protection, cybersecurity, regulation compliance, governance, responsible AI and monitoring of data and AI solutions. Collaborate with network engineering, product, operations, software development and data science partners to translate requirements into reliable technical solutions. Adapt quickly to evolving business priorities, balancing multiple initiatives while maintaining solution quality and delivery commitments.  

Proactively identify risks, dependencies, and opportunities for improvement, escalating issues early and driving resolutions independently.

Skills & Qualifications Strong proficiency in SQL and Python or similar programming languages, with experience in data engineering, data quality, data product development or application development and data analytics. Hands-on experience building production data applications using AWS, Databricks, Spark, or comparable cloud and distributed-processing technologies. Deep experience building, monitoring, and optimizing large scale data pipelines and data-intensive applications. Working knowledge of machine learning workflows, including model development, deployment, evaluation, and monitoring.

Familiarity with ML or data science models, AI-enabled analytics, LLM, RAG applications, AI agents, or model tuning and fine-tuning workflows.

Experience with system design, data architecture design, integration, version control, CICD, testing, deployment, and production support.

Experience with business intelligence tools such as Tableau or Power BI for reporting and data visualization, EDA, data analysis. Knowledge of metadata…