Platform Data Engineer

Comcast

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
West Chester, PA
Posted
25 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $170k
$118k most similar roles pay here $221k

This listing doesn't post a salary. Most similar roles pay $127,987–$211,200.

Based on 240 similar postings.

Employer

About Comcast

Comcast is an American telecommunications and media conglomerate, providing cable TV, internet, and phone services under the Xfinity brand, and owning NBCUniversal.

Comcast currently has 84 open roles on FindRole.

Listed pay typically runs $137,369–$206,053 across 58 roles with salary data.

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At a glance

TL;DR · Platform Data Engineer

As a Platform Data Engineer - (DataBricks, PySpark, AWS), you will join the Data Product Engineering team to manage and evolve an enterprise Data Lake supporting critical workforce, billing, and interaction datasets. You will design, develop, and optimize scalable data pipelines while building cloud-native solutions for high-volume data processing. Your daily work involves developing batch and streaming frameworks, performing performance tuning, and providing production support for large-scale distributed systems. The role requires proficiency in AWS, PySpark, Databricks, Kafka, Kubernetes, and Python, with specific experience using MWAA to orchestrate workflows. You will solve complex problems involving high-concurrency environments and tens of terabytes of data to enable analytics and business decision-making. Additionally, you will mentor junior engineers and contribute to the long-term evolution of the enterprise data platform architecture.

What you'll do

  • Design, develop, and optimize scalable data pipelines for workforce, billing, and interaction datasets.
  • Build and enhance cloud-native data solutions using AWS, Databricks, and PySpark.
  • Develop batch and streaming data processing frameworks to integrate source systems into modern architectures.
  • Ingest and process high-volume data in near real-time using Kafka and Databricks streaming.
  • Orchestrate and manage complex data workflows using Managed Workflows for Apache Airflow (MWAA).
  • Provide production support, troubleshooting, and root-cause analysis for critical data workflows.
  • Drive performance tuning, automation initiatives, and operational improvements across the data platform.
  • Mentor junior engineers and provide technical guidance to team members.

What we're looking for

  • 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, and Databricks.
  • Experience building and maintaining large-scale ETL/ELT pipelines and distributed systems for high-volume data processing.
  • Proficiency in Python development within data engineering environments.
  • Experience orchestrating workflows using Apache Airflow or MWAA.
  • Knowledge of Kubernetes fundamentals including pod lifecycle and job orchestration.
  • Experience with Snowflake, Kafka, streaming architectures, or Amazon EKS (preferred).

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