Senior Cloud Data Engineer - AWS, PySpark & Databricks

Comcast Corporation

Chester (Delaware County)

On-site

USD 120,000 - 170,000

Full time

6 days ago

Be an early applicant

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Job summary

Comcast Corporation is seeking a Data Engineer to join our Data Product Engineering team, overseeing an enterprise Data Lake for critical datasets across the GTO organization. You will design and optimize scalable pipelines and cloud-native solutions using AWS, PySpark, and Databricks to support analytics, reporting, and business decisions.

The ideal candidate has 5+ years in data engineering, strong Python capability, and experience with Airflow/MWAA and Kubernetes.

Qualifications

Bachelor's degree in Computer Science, Engineering, Information Systems, or 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; Proficiency in Python development within data engineering environments; Experience supporting production data platforms and driving operational excellence.
Strong communication and collaboration skills with the ability to work effectively across multiple teams.

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.