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CVS Health is at the forefront of digital transformation. The company is embarking on a journey to evolve its existing Extra Care program into a world-class personalization and loyalty program.

This is a top initiative within CVS Health. They already invested in state-of-the-art technology and scaling of their loyalty program and now they are focused on optimizing their customer contact strategy.

CVS Health’s Data Engineering team is helping lead this personalization effort and has a new opening for a data engineering leader.

The Sr. Data Engineering Architect will lead a team of advanced data engineers to design, build, test, productionalize and support components of the Front Store Personalization engine. This role will require an understanding and management of the source data, feature development, ML models and pipelines, business rules, the orchestration and productionalization of ML pipelines, structured experimentation in support of iterative testing and learning, and maintenance and enhancements of ML pipelines over time to support an expanding set of personalization use cases.

CVS is continuing on its journey to deliver their customers’ 1:1 personalized experiences through multiple channels. To support this effort, this Engineering leader will work across multiple teams to rapidly building, testing, and scaling high-priority use cases that drive increased reach, relevance and rewards for their customers.

Responsibilities include:
Lead coding and architecting of end-to-end applications on modern data processing technology stack (e.g. Hadoop, Cloud, Spark, Azure, Databricks)

Manage multiple projects and lead collaborative reviews of design, code, data, feature implementation performed by other data engineers in support of maintaining data engineering standards

Build a process to drive continuous integration/continuous delivery, test-driven development, and production deployment frameworks

Troubleshoot complex data, features, personalized offer build rules issues and perform root cause analysis to proactively resolve product and operational issues (i.e., primary languages Python, Scala and L2-3 production support)

Productionalize the full pipeline including distributed Machine Learning models (e.g., training/test pipeline, offer eligibility, data layer, feature layer, etc.)

Connect business context and perspective to define model objective functions, features, business rules, prioritization, measurement, etc.

Lead conversations with infrastructure teams (on-prem & cloud) on analytics application requirements (e.g., configuration, access, tools, services, compute capacity, etc.)

Identify the skills and experience needed for Data Engineers, Machine Learning Engineers, and adjacent roles, and work with leadership to make required hiring decisions as the process evolves over time.

Qualifications:
8+ years of Big Data, Machine Learning, and Spark experience building and running products and applications at scale, in production, in mission-critical situations

3+ years leading data engineers and/or analytics-focused teams to deliver complex analytics projects on aggressive timelines

Full-time, 100% dedicated to Personalization Lab, ideally co-located with Lab in Customer Support Center.
Platforms: Azure Cloud, Databricks, Hadoop / HDInsight, Spark, Oracle, TD, IntelliJ
• Languages: PySpark, Python, Shell Scripting, SQL, Pig, Java / Scala
• Proficient in Map-Reduce, Spark, Jenkins, Hbase, Pig, No-SQL, Git
• Experience with building data pipelines, data modeling, architecture & governance concepts
• Experience implementing ML models and building highly scalable and high availability systems
• Experience operating in distributed environments including cloud (Azure, GCP, AWS etc.)
• Experience building, launching and maintaining complex analytics pipelines in production

Preferred:
Experience working via an agile, sprint-based working style

Experience working side-by-side with business owners, and translating business needs into analytics solutions

Proven ability to successfully balance near-term results (e.g., ability to design and execute on a ‘MVP’ model), with long-term goals

Comfortable balancing quality of output with short timelines required to enable downstream functions