This role pays more than
56%
of similar roles. Most pay
$130,500–$211,200
— the shaded band above.
At the midpoint, this role pays about
$185k
versus about
$171k
for comparable roles.
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
88 open roles
on FindRole.
Listed pay typically runs
$141,850–$203,546
across 63 roles with salary data.
The Sr. Data Engineer - Freewheel joins the Audience & AI group as part of the Revenue Science engineering team to support data engineering efforts for analysis, machine learning, optimization methods, and statistical model building. This role involves collaborating with cross-functional teams to define data-driven questions while designing and building data pipelines that ingest, standardize, and transform logs into warehouse tables. The candidate will develop automated processes for data integration, manage infrastructure at scale, and ensure quality through CI/CD and monitoring. Key technologies include AWS, Databricks, Snowflake, Kubernetes, Teradata, Datadog, and Airflow. Additionally, the role requires experience with LLMs, LangChain, and MLOps. The position focuses on solving technical challenges within the advertising technology domain to develop innovative approaches for next-generation ad tech products through robust data architecture and sophisticated engineering practices.
What does a Data Engineer earn in New York?
Median $211200 from 30 postings across 10 companies.
Engineer and refine algorithms and automated processes for robust data integration and cleansing from multiple sources.
Design and build data pipelines to ingest, standardize, and transform logs into warehouse tables and feeds.
Create scalable data architectures to manage the ingestion, processing, and storage of large datasets.
Ensure data quality across the pipeline through CI/CD, monitoring, and troubleshooting failures.
Optimize compute and storage usage throughout model pipelines for all team members.
Develop systems to detect and remediate security and code vulnerabilities.
Support data engineering efforts related to AI, machine learning, optimization methods, and statistical model building.
What we're looking for
Bachelor’s degree in a technical field such as Data Science, Computer Science, Engineering, Business Analytics, Mathematics, Statistics, or Operations Research required.
Master’s or PhD degree is preferred.
At least 7 years of experience in data engineering, including experience using large datasets.
Experience using AWS, Databricks, and Snowflake technologies is highly preferred.
Knowledge of AI platforms (e.g., Amazon Bedrock), LLMs, and AI orchestration tools like LangChain or LangGraph is desired.
Knowledge of MLOps and LLMOps processes is desired.
Experience with infrastructure and monitoring tools such as Kubernetes, Teradata, Datadog, and Airflow.
Skilled in data mangling, ETL pipelines, and CI/CD processes.
Python
Scala
Java
SQL
Azure
AWS
GCP
Azure Synapse
Azure Databricks
Redshift
Big Query
Snowflake
Azure Data Factory
Cosmos DB
Event Hubs
Azure Storage
Azure Functions
Container Apps
CI/CD
Azure DevOps
GitHub
Git
ETL
ELT
Data Modeling
Data Warehousing
Data Lake
Test Driven Development
Python
SQL
Scala
Java
Azure
AWS
GCP
Microsoft Fabric
Azure Databricks
Snowflake
Big Query
Azure Data Factory
Air Flow
Cosmos DB
Azure Functions
GitHub
Git
CI/CD
Azure DevOps
Data Modeling