Data Engineer

CVS Health•Woonsocket, RI

4d•$130,832 - $144,200•Hybrid

About The Position

Caremark, LLC., a CVS Health company, is hiring for the following role in Woonsocket, RI: 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, Analytics, or a related field.
Completion of a university-level course, research project, internship, or thesis in Programming in Java, Python, or R.
Completion of a university-level course, research project, internship, or thesis in SAS or SQL programming languages.
Completion of a university-level course, research project, internship, or thesis in Spark, PySpark, or Scala.
Completion of a university-level course, research project, internship, or thesis in Hadoop architecture and HDFS commands.
Completion of a university-level course, research project, internship, or thesis in Visualization tools: PowerBI or Tableau.
Completion of a university-level course, research project, internship, or thesis in Machine learning, statistical analysis, and predictive modeling.
Completion of a university-level course, research project, internship, or thesis in Relational database concepts.
Completion of a university-level course, research project, internship, or thesis in Designing data models and solutions for analytical and reporting use cases.
Completion of a university-level course, research project, internship, or thesis in Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation.
Completion of a university-level course, research project, internship, or thesis in Quantitative analysis techniques, including clustering, regression, and pattern recognition.

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.