Senior Data Engineer: Scalable Pipelines & Data Quality

Comcast Corporation; Reston, Northern (VA, KY); Hybrid; USD 104,000 - 170,000; Full time

Benefits offered by this job

Benefits package

Bonus eligibility

Job summary

Comcast’s FreeWheel division in Reston, VA seeks a data engineer to design, develop, and manage advanced data structures and pipelines for reliable business insights.

The role requires strong SQL, Python, Spark, and cloud data warehouse experience, with familiarity in Looker and modern data tools as a plus. Collaboration across teams will drive scalable data solutions on AWS, Databricks, and Kubernetes.

Qualifications

• Strong SQL proficiency.
• Programming language proficiency (Python preferred).
• Proficiency in distributed computing frameworks (e.g., Apache Spark).
• Cloud Data Warehouse management.
• Knowledge of at least one cloud provider (AWS, GCP, or Azure).
• Familiarity with data pipeline design pattern.
• Knowledge of data observability.
• Exposure to Looker or Embedded BI is a plus.
• Exposure to dbt, Airflow, Snowflake, Spark is desirable.
• Exposure to Data Consumption challenges and solutions.

Responsibilities

• Designing and constructing data architectures and pipelines to standardize, transform, and ensure data integrity.
• Guaranteeing data quality throughout ingestion, processing and loading phases.
• Engineering robust ingestion frameworks for varied data types and reporting data quality.
• Developing accessible methods for data consumption, including APIs and views.

Skills

Data Lake

Data Lakehouse Architecture

Data Structures

Data Warehousing

Python

SQL

Education

Bachelor's Degree

Job description

Comcast’s FreeWheel division in Reston, VA seeks a data engineer to design, develop, and manage advanced data structures and pipelines for reliable business insights.

The role requires strong SQL, Python, Spark, and cloud data warehouse experience, with familiarity in Looker and modern data tools as a plus. Collaboration across teams will drive scalable data solutions on AWS, Databricks, and Kubernetes.