Responsibilities:

Design, develop, and optimize conceptual, logical, and physical data models across diverse platforms (RDBMS, NoSQL, and cloud-native databases).
Model transactional data structures in Oracle, Aurora, DynamoDB, and Document DB.
Translate legacy/on-prem relational models into cloud-based models (Aurora/DynamoDB/Document DB).
Design and implement dimensional models (star/snowflake) and Data Vault 2.0 for analytical workloads in the conformed zone.
Create Unified Star Schema (USS) and other optimized dimensional models for reporting/analytics in the published zone.
Ensure data models can scale to handle 500M+ records with high-speed write throughput (raw conformed layers) and read-optimized designs for published layers.
Leverage open table formats in Databricks Lakehouse (Delta Lake/ Iceberg) for modeling across raw, conformed, and published layers.
Maintain data dictionaries, metadata repositories, and model documentation using industry-standard tools such as Erwin, Power Designer, or equivalent.
Collaborate with architects and business stakeholders to ensure models align with business requirements and data governance standards.
Data model design and development: Develop and maintain conceptual, logical, and physical data models to support business needs for analytics, operational applications, and reporting.
Requirements gathering: Work with stakeholders, product owners and system analysts to understand business processes, data needs, and reporting requirements.
Data integration: Design data mapping specifications and work with data engineers and system analysts to integrate data from diverse sources into data vault implementations, data warehouses or data lakes.
Data quality and integrity: Ensure the accuracy, consistency, and completeness of data by establishing validation rules and constraints.
Performance optimization: Identify and address performance bottlenecks in data models and optimize database queries for faster retrieval and processing.
Documentation and governance: Create and maintain detailed documentation, including data dictionaries, data flow diagrams, and metadata repositories. Uphold data governance standards and best practices.
System evaluation: Review and evaluate existing data systems for efficiency, discrepancies, and scalability.
A key aspect of this responsibility is ensuring that data is accurately cleaned, transformed, and loaded to enable consistent and reliable analytics and reporting. Very high-quality data is essential to our business foundation.
Enable a 360-degree view of customer-centric information through integration of a multitude of internal/external systems, mobile apps, devices, and data marts.
Support and enhance existing Individual Family Retail Health Insurance applications used by consumers as well as operations staff.
Participate in all agile ceremonies effectively.
Ability to mentor and coach a team of jr. developers.

Data Modeling Advisor - HIH - Evernoth