Platform Data Engineer - (DataBricks, PySpark, AWS)

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 We are seeking a Data Engineer(Engineer 3) to join our Data Product Engineering Team team responsible for managing and evolving the enterprise Data Lake that supports critical datasets across the GTO organization. This team owns large-scale workforce, billing, and interaction datasets and is focused on building scalable, reliable, and high-performance data solutions that enable analytics, reporting, and business decision-making. The ideal candidate will have strong experience building and optimizing distributed data pipelines in cloud environments, working with high-volume datasets, and partnering with cross-functional teams to deliver impactful data products. This role offers the opportunity to work with environments processing over 50TB of interaction data, leveraging modern technologies including AWS, PySpark, Databricks, Kafka, Kubernetes, and Airflow.

Key Responsibilities Design, develop, maintain, and optimize scalable data pipelines supporting workforce, billing, interaction, and other enterprise datasets. Build and enhance cloud-native data solutions using AWS, Databricks, and PySpark. Develop and support batch and streaming data processing frameworks, integrating source systems and interfaces through modern data architectures. Leverage technologies such as Kafka and Databricks streaming solutions to ingest and process high-volume data in near real-time. Drive data pipeline performance tuning, automation initiatives, and operational improvements across the platform. 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 (Managed Workflows for Apache Airflow) to orchestrate and manage data workflows. Collaborate closely with Product, Data Governance, Analytics, and Engineering teams across both onshore and offshore delivery models. Support data warehousing initiatives and help establish best practices for data quality, scalability, and reliability. Mentor junior engineers, provide technical guidance, and contribute to the growth and development of Engineering I team members. Participate in architectural discussions and contribute to the long-term evolution of the enterprise data platform.

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 PySpark Databricks Experience building and maintaining large-scale ETL/ELT pipelines. Strong understanding of distributed systems and large-volume data processing. Experience with data warehousing concepts and modern data architectures. Experience orchestrating workflows using Apache Airflow/MWAA. Knowledge of Kubernetes fundamentals, including pod lifecycle, job orchestration, and workload configuration.