Data Engineer

CVS Health•Irving, TX

4d•Hybrid

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary Analyze data engineering problems and 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. Hybrid position: remote work permitted but must live within commuting distance of designated office location and remain available to report to office as needed and required.

Requirements

Master’s degree (or foreign equivalent) in Computer Science, Information Technology, Statistics, Analytics, Engineering, or a related field.
Two (2) years of experience in the job offered or a related occupation.
Two (2) years of experience with “Big data” platforms including: Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
Two (2) years of experience with SAS or SQL programming languages.
Two (2) years of experience with Visualization tools, including PowerBI or Tableau.
Two (2) years of experience with Spark, PySpark, or Scala.
Two (2) years of experience with Extract/Transform/Load (ETL) processes.
Two (2) years of experience writing application code and deploying to production.
Two (2) years of experience developing backend services, performing code reviews, and collaborating with peers on software development solutions.

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.