Data Engineer, Platform

About The Company

Join Comcast, a Fortune 30 global media and technology leader renowned for its innovative approach to connectivity, content creation, and digital platforms. With a broad reach that spans hundreds of millions of customers, viewers, and guests worldwide, Comcast is committed to transforming the media landscape through cutting-edge products and services. Our award-winning technology teams work tirelessly to turn big ideas into impactful solutions, fostering an environment that encourages innovation, collaboration, and diversity. We value authentic self-expression and invest in our employees’ growth, ensuring they have the resources and support needed to excel in their careers. At Comcast, you will have the opportunity to do your best work in a dynamic, inclusive environment that celebrates creativity and technological advancement.

About The Role

We are seeking a highly skilled Data Engineer (Engineer 3) to join our Data Product Engineering Team. This team is pivotal in managing and evolving the enterprise Data Lake, which supports critical datasets across the Global Technology Operations (GTO) organization. The Data Lake encompasses large-scale workforce, billing, and interaction datasets, and the team’s primary focus is on building scalable, reliable, and high-performance data solutions that enable advanced analytics, reporting, and strategic business decision-making.

The successful candidate will have extensive experience in designing and optimizing distributed data pipelines within cloud environments, working with high-volume datasets, and collaborating across multiple teams to deliver impactful data products. This role offers the chance to work with environments processing over 50TB of interaction data, utilizing modern technologies such as AWS, PySpark, Databricks, Kafka, Kubernetes, and Airflow, to support enterprise-wide data initiatives.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience
• 5+ years of experience in Data Engineering, Data Platform Engineering, or related disciplines
• Strong hands-on experience with AWS cloud services
• Proficiency in PySpark and Databricks platform
• Experience building and maintaining large-scale ETL/ELT pipelines
• Deep understanding of distributed systems and high-volume data processing
• Knowledge of data warehousing concepts and modern data architecture principles
• Experience orchestrating workflows using Apache Airflow or MWAA
• Familiarity with Kubernetes fundamentals, including pod lifecycle and workload management
• Proficiency in Python development within data engineering environments
• Experience supporting production data platforms and driving operational excellence
• Strong communication and collaboration skills to work effectively across teams

Responsibilities

• Design, develop, maintain, and optimize scalable data pipelines supporting enterprise datasets such as workforce, billing, and interactions
• Create and enhance cloud-native data solutions leveraging AWS, Databricks, and PySpark
• Develop and support batch and streaming data processing frameworks, integrating source systems through modern architecture patterns
• Utilize Kafka and Databricks streaming solutions to ingest and process high-volume data in near real-time
• Drive performance tuning, automation initiatives, and operational improvements across data pipelines
• Provide production support, troubleshooting, and root cause analysis for critical data workflows
• Work with large-scale distributed systems and high-concurrency environments processing tens of terabytes of data
• Utilize MWAA to orchestrate and manage complex data workflows
• Collaborate with Product, Data Governance, Analytics, and Engineering teams across onshore and offshore delivery models
• Support data warehousing initiatives and establish best practices for data quality, scalability, and reliability
• Mentor junior engineers, provide technical guidance, and contribute to team growth
• Participate in architectural discussions and contribute to the long-term evolution of the enterprise data platform

Benefits

• Comprehensive health, dental, and vision insurance plans
• Retirement savings options including 401(k) plans with company matching
• Paid time off and holidays to promote work-life balance
• Career development programs and continuous learning opportunities
• Flexible work arrangements where applicable
• Employee assistance programs supporting physical, emotional, and financial well-being
• Inclusive workplace culture that values diversity and innovation