Data Engineer

Job Description:

Required skills:-Python, SQL, data pipeline fundamentals (transformations, aggregations), and GCP (BigQuery, warehouse modeling i.e. fact and dimension tables) 5+ years of experience - relatively Senior level, self-starter, doesn't need hand-holding.

Overview: Have multiple concurrent initiatives, all of which requiring Data Engineering support to work closely with their Data Science team: compute infrastructure migration, tech debt reduction, expansion of tool into new use case.

 

2 products they develop and maintain on his team, both in support of Caremark and PBM as a business and both in support of underwriting and client consulting stakeholder:

ACCT Price Benchmarking Tool - Suite of tools to help them understand Caremark deal values. Deals consist of ~50 parameters to define key pricing terms, driving complexity to determine whether a deal is "good" or "bad." ACCT takes pricing components and other parameters, data from clients similar to one another, and generates a comparison.
Excel based with a pipeline built by their data science team (unsure how specifically). Needs to be finished porting to GCP Dataflow, refine the pipeline, and a bunch of other enhancements.
Could support new feature development once the backlog is complete.
Data ingestion coming from 2 different sources:
A legacy system that is excel file based, parsing and scraping excel files.
EOS, modern infra with a GCP back end. Have 2 pipelines puling from both sources into GCP for unified source of truth.
Caremark benefit engine: tool used to make recommendations for how clients could change benefit structures to achieve cost targets. How do changes affect members? How would cost structure change if parameters are updated? Overarching goal is to make it a client-facing product, rolling out 2027 (currently is only internal).
Legacy SAS code that needs to be migrated into Python and others
Provide support for modeling work - feature engineering, building datasets for data science teams.