The primary focus is validating the quality, integrity, reliability, and consistency of data moving across pipelines, transformations, and warehouse layers, ensuring accuracy before consumption by analytics platforms. Key tasks involve writing advanced SQL, investigating discrepancies, performing root cause analysis, and developing automated validation checks using Python.

The role involves designing, building, and maintaining high-performance data pipelines using tools like SQL, dbt, and Alteryx, while owning the configuration and optimization of Alteryx Server/Cloud and Tableau Server/Cloud environments at scale. Responsibilities also include implementing monitoring, ensuring data quality, building security and compliance into solutions, and acting as an escalation point for L2/L3 support.

The role involves building and managing large-scale data systems, designing data-intensive applications, and engineering solutions for complex entity mastering and resolution within a HIPAA-compliant healthcare environment. Responsibilities also include developing scalable data pipelines, managing Big Data solutions, and evolving the Lakehouse platform for analytics and machine learning.

The Data Engineer will design and build production-grade data pipelines and platforms, ingesting, cleaning, and organizing operational data from various sources like ServiceNow, mainframe, and cloud environments to power predictive services and intelligent automation. This role involves partnering with AI/ML teams to deliver feature pipelines and supporting ML production workflows, with a focus on operational relevance and cost optimization.