IQ Workforce is a leading recruiting firm for the analytics and data science communities.

CVS Health is at the forefront of digital transformation in healthcare. “Pharmacy Personalization” is a major initiative to transform the customer experience within their retail pharmacies. The goal is to help patients maintain adherence with their medications – one of the factors that are most likely to affect their health outcomes.

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 their Pharmacy Personalization engine. This role includes the data, feature, machine learning model, and business rule components of the analytics pipeline, the orchestration and productionalization of that pipeline, the structured experimentation in support of iterative testing and learning, and maintenance and enhancements of that pipeline over time to support an expanding set of Pharmacy personalization use cases. This leader will collaborate with Data Scientists and associated business partners to accomplish the execution of the data and analytic roadmap for Pharmacy Personalization.

Qualifications:
Experience leading data engineers and/or analytics-focused teams to deliver complex analytics projects on aggressive timelines

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

Full-time, 100% dedicated to Personalization Lab, ideally co-located with Lab in Customer Support Center
• Platforms: Azure Cloud, DataBricks, Hadoop, Spark, Kafka, Kinesis, Oracle, TD
• Languages: PySpark, Python, Hive, Shell Scripting, SQL, Pig, Java / Scala
• Proficient in Map-Reduce, Spark, Airflow / Oozie / Jenkins, Hbase, Pig, No-SQL, Chef / Puppet, Git
• Familiarity 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

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