Data Engineer

CVS Health•New York, NY

4d•$118,102 - $173,040•Remote

About The Position

Aetna Resources, LLC, a CVS Health company, is hiring for the following role in New York, NY: Data Engineer to develop, build and manage large-scale data structures, pipelines and efficient Extract/Load/Transform (ETL) workflows to address complex problems and support business applications. Duties include: develop large scale data structures and pipelines to organize, collect and standardize data to generate insights and addresses reporting needs; write ETL (Extract/Transform/Load) processes, design database systems, and develop tools for real-time and offline analytic processing that improve existing systems and expand capabilities; collaborate with Data Science team to transform data and integrate algorithms and models into automated processes; test and maintain systems and troubleshoot malfunctions; leverage knowledge of Hadoop architecture, HDFS commands, and designing and optimizing queries to build data pipelines; utilize programming skills in Python, Java, or similar languages to build robust data pipelines and dynamic systems; build data marts and data models to support Data Science and other internal customers; integrate data from a variety of sources and ensure adherence to data quality and accessibility standards; analyze current information technology environments to identify and assess critical capabilities and recommend solutions to complex business problems; and experiment with available tools and advise on new tools to provide optimal solutions that meet the requirements dictated by the model/use case. Telecommuting available. Multiple positions.

Requirements

Master’s degree (or foreign equivalent) in Computer Science, Data Science, Statistics, Mathematics, Physics, Analytics, Information Technology or a related field and completion and two (2) years of experience in the job offered or related field.
Jenkins, GIT, Python, and SQL
Spark, PySpark, and Airflow
Machine learning, statistical analysis, and predictive modeling
Relational database concepts
Extract/Transform/Load (ETL) processes
Cloud components including cluster management
Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation
Machine learning operations such as model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, and orchestration
Quantitative analysis techniques, including clustering, regression, and pattern recognition
Developing and deploying predictive models or ML systems in a cloud environment (GCP, AWS, or Azure)

Responsibilities

Develop large scale data structures and pipelines to organize, collect and standardize data to generate insights and addresses reporting needs.
Write ETL (Extract/Transform/Load) processes, design database systems, and develop tools for real-time and offline analytic processing that improve existing systems and expand capabilities.
Collaborate with Data Science team to transform data and integrate algorithms and models into automated processes.
Test and maintain systems and troubleshoot malfunctions.
Leverage knowledge of Hadoop architecture, HDFS commands, and designing and optimizing queries to build data pipelines.
Utilize programming skills in Python, Java, or similar languages to build robust data pipelines and dynamic systems.
Build data marts and data models to support Data Science and other internal customers.
Integrate data from a variety of sources and ensure adherence to data quality and accessibility standards.
Analyze current information technology environments to identify and assess critical capabilities and recommend solutions to complex business problems.
Experiment with available tools and advise on new tools to provide optimal solutions that meet the requirements dictated by the model/use case.