Data Engineer

CVS Health•Wellesley, MA

4d•$140,130 - $173,040•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: CVS Pharmacy, Inc., a CVS Health company, is hiring for the following role in Wellesley, MA: Data Engineer to design, build and manage large scale data structures, pipelines and efficient Extract/Load/Transform (ETL) workflows to 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; collaborate with Data Science team to transform data and integrate algorithms and models into automated processes; 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; 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
Data collection, storage, tidying, transformation, processing, management, and modeling (6 months experience or university-level course, research project, internship, thesis)
Supervised machine learning (6 months experience or university-level course, research project, internship, thesis)
Programming in Python, R, or MATLAB (6 months experience or university-level course, research project, internship, thesis)
Unsupervised machine learning and data mining (6 months experience or university-level course, research project, internship, thesis)
Algorithmic design paradigms and techniques (6 months experience or university-level course, research project, internship, thesis)
Web technologies including ASP.NET, C#, HTTP, CSS, XML, XSLT, JavaScript, AJAX, RSS/Atom, SQL, and web services (6 months experience or university-level course, research project, internship, thesis)
Open source cloud platforms (6 months experience or university-level course, research project, internship, thesis)
Key big data platforms (6 months experience or university-level course, research project, internship, thesis)
Data center scale systems (6 months experience or university-level course, research project, internship, thesis)

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
Collaborate with Data Science team to transform data and integrate algorithms and models into automated processes
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
Experiment with available tools and advise on new tools to provide optimal solutions that meet the requirements dictated by the model/use case