Data Engineer

CVS Health

Remote

Quick summary

Work type: Remote
Location: New York, NY
Posted: 6 days ago
Closes: Jul 6, 2026
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $163k

$110k most similar roles pay here $210k

This listing doesn't post a salary. Most similar roles pay $126,800–$199,050.

Based on 240 similar postings.

Employer

About CVS Health

CVS Health is a leading American healthcare company operating retail pharmacies, pharmacy benefit management services, and a health insurance segment through Aetna, one of the nation''s largest health insurers. Industry: Healthcare & Pharmacy

CVS Health currently has 407 open roles on FindRole.

Listed pay typically runs $118,450–$284,280 across 133 roles with salary data.

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At a glance

TL;DR · Data Engineer

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Aetna Resources, LLC, a CVS Health company, seeks a Data Engineer with a Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Physics, Analytics, or Information Technology for its New York team. This role involves developing and managing large-scale data structures and pipelines to support complex business applications through efficient ETL workflows. Day-to-day responsibilities include using Jenkins, GIT, Python, SQL, Spark, PySpark, and Airflow to build scalable solutions, conducting feature engineering and model training, implementing supervised and unsupervised learning, and deploying predictive models in cloud environments like GCP, AWS, or Azure. The ideal candidate will have experience with machine learning operations such as versioning, monitoring, and deployment, along with expertise in quantitative analysis techniques including clustering and regression.

Skills

Python SQL Spark PySpark Airflow Jenkins GIT Google Cloud Platform AWS Azure Feature engineering Model training Hyperparameter tuning Distributed model training Supervised learning Unsupervised learning Machine learning operations Quantitative analysis Clustering Regression Pattern recognition

What you'll do

Develop and manage large-scale data structures and pipelines using Python and SQL.
Implement efficient ETL workflows to address complex business problems.
Utilize Spark, PySpark, and Airflow for data processing and workflow management.
Conduct feature engineering and model training in a cloud environment (GCP, AWS, Azure).
Deploy predictive models and ML systems with versioning and monitoring capabilities.
Apply quantitative analysis techniques such as clustering, regression, and pattern recognition.

What we're looking for

Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Physics, Analytics, Information Technology or related field required.
2 years of experience with Jenkins, GIT, Python, and SQL.
2 years of experience with Spark, PySpark, and Airflow.
2 years of expertise in feature engineering, model training, hyperparameter tuning, distributed model training, and supervised/unsupervised learning implementation.
Experience in machine learning operations including versioning, lineage, monitoring, hosting, deployment, scalability, and orchestration.
Proficiency in quantitative analysis techniques such as clustering, regression, and pattern recognition.

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