Platform Data Engineer (Databricks, PySpark)

Salary: $63,000 - 94,500 per year Requirements:

• Bachelors degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience; relevant coursework or extensive professional experience may also be considered.
• 5+ years of experience in Data Engineering, Data Platform Engineering, or a related discipline; the posting lists 5–7 years of relevant work experience.
• Hands-on experience with AWS, PySpark, and Databricks.
• Experience building and maintaining large-scale ETL/ELT pipelines and working with distributed systems and high-volume data processing.
• Understanding of data warehousing concepts and modern data architectures.
• Experience orchestrating workflows with Apache Airflow or MWAA.
• Knowledge of Kubernetes fundamentals, including pod lifecycles, job orchestration, and workload configuration.
• Proficiency in Python for data engineering.
• Experience supporting production data platforms and improving operational practices.
• Strong communication and collaboration skills across teams.
• Preferred: Experience with Snowflake, Kafka and streaming architectures, Amazon EKS, or large-scale interaction, advertising, marketing, or customer engagement datasets.
• Preferred: Experience implementing data platform automation, observability, and monitoring, and mentoring junior engineers or leading technical initiatives.

• Design, develop, maintain, and optimize scalable pipelines for 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 processing frameworks, integrating source systems through modern data architectures.
• Use Kafka and Databricks streaming solutions to ingest and process high-volume data in near real time.
• Tune pipeline performance and drive automation and operational improvements across the platform.
• Provide production support, troubleshoot critical data workflows, and identify root causes.
• Work with distributed systems and high-concurrency environments processing tens of terabytes of data.
• Orchestrate and manage data workflows using MWAA, the Managed Workflows for Apache Airflow service.
• Collaborate with Product, Data Governance, Analytics, and Engineering teams across onshore and offshore delivery models.
• Support data warehousing initiatives and establish practices for data quality, scalability, and reliability.
• Mentor junior engineers, provide technical guidance, and support the development of Engineering I team members.
• Participate in architectural discussions and help guide the long-term evolution of the enterprise data platform.

• Airflow
• AWS
• Cloud
• Databricks
• ETL
• Support
• Kafka
• Kubernetes
• Marketing
• Python
• PySpark
• Snowflake
• Web

More:

We are hiring a Data Engineer (Engineer 3) for Comcasts Data Product Engineering team, which manages and evolves the enterprise Data Lake supporting critical datasets across the GTO organization. Our platform serves workforce, billing, and interaction data, including environments processing more than 50 TB of interaction data, and enables analytics, reporting, and business decisions. Comcast is a Fortune 30 global media and technology company with an award-winning technology team; we encourage innovation, recognize and invest in ideas, and aim to create an inclusive workplace where employees can bring their authentic selves. We generally prefer on-site collaboration unless a team is designated as virtual; for roles listing both office and virtual options, candidates living more than 100 miles from an office may be considered for remote work. Compensation includes base pay and, depending on the role, bonus or commission eligibility. Eligible employees can access a range of benefits and personalized support for physical, financial, and emotional well-being. We are an equal opportunity employer and consider qualified applicants in accordance with applicable law.

last updated 42 week of 2026