CVS is embarking on a journey of evolving its existing ExtraCare program into a world-class personalization and loyalty program. This is a top initiative within the company and we have a team dedicated to recruiting the best talent in the world to help propel us to this goal. The company has already invested in state-of-the-art technology and scaling of our loyalty program, now we are focused on optimizing our customer contact strategy. We are looking for the best and brightest to in our existing Analytics team and help deliver on this initiative.
CVS Health is seeking a Data Engineer – Front Store Analytics to further enhance Enterprise Analytics support of the Loyalty and Personalization organization. The Data Engineer will work in an agile team responsible for bringing the CVS Health's front store Personalization efforts to life. The ideal candidate should be process oriented, with a solid foundation in cloud architecture, having the ability to both provide technical expertise as well as work cross-functionally in support of the business, analytics and the IT organization. This position is based out of our Woonsocket, RI office and will report to the Lead Data Engineer, Front Store Analytics within the Enterprise Analytics organization.
• Develop and release ML pipelines into a production environment using Spark and Databricks (primary languages: Scala, Python and SQL). Enable the integration of ML pipelines and refine the processes and tools with existing CICD framework/processes for Personalization Engine environment.
• Optimize code developed by to deploy CVS machine learning applications as a Spark application in large scale distributed environment
• Define & implement folder structure in the Data Lake for model management and SQL DB for rules repositories.
• Validate integrations with upstream data sources, provisioning controls and enforce provisioning quotas and generation of alerts
• Support Data Science by integrating models for deployment, feature development, and optimizing code for pipelines.
• Work with data scientists to understand and validate research objectives to optimize data science
• Ensure end-to-end process integration and validate output. Establish and supportinfrastructure for unit testing
• Update design documentation for the Personalization Engine as needed
• 4+ years proven business experience and technical expertise in data infrastructure roles, supporting cloud infrastructure.
• 4+ years of experience in data warehousing, optimization, and productionalization with examples of increased responsibility and evolving technologies.
• 4+ years of experience developing code and/or applications (e.g., Pyspark, PySQL, Scala, etc…)
• 2+ years of experience deploying machine learning and data science pipelines into production using model management solutions and leveraging CICD solutions (e.g., Jenkins) for automation
• 2+ years of experience configuring cloud platforms and configuring elastic compute environments in a cloud platform
• Familiarity with and understanding of modern machine learning approaches, algorithms, libraries, and processes for feature selection / engineering
• Experience building containerized applications and deploying those applications using solutions like Kubernetes
• Experience working in a matrix organization (e.g., Data Sciences, IT, Marketing, etc…) to define and implement an automated Personalization Engine. Strong experience in roles requiring analytical, critical thinking, problem solving and communication skills.
• A history of developing usable system and architecture documentation.
• MCSA – Cloud Platform, MCSE – Cloud Platform and Infrastructure, or MCSD – Azure Solutions Architect
• 3+ years of experience with implementing a fully productionalized Data Science solution in Azure.
• 1+ years of exposure to Databricks and Intelli J
• Personalization technology experience (e.g., Teradata, IBM Unica, Adobe, Dunnhumby, Oracle, Datalogix)
• Excellent communication and presentation skills with the ability to present complex information in a concise and compelling manner
• Experience with loyalty programs and Retail
• Bachelor's degree in a quantitative field (e.g., information systems, computer science, engineering)
• Technical Master's degreepreferred