Analytics Data Modeler Lead

Overview

Analytics Data Modeler Lead at Genworth

The Analytics Data Modeler Lead transforms raw data into meaningful business insights. This role designs, develops, and maintains robust data models that empower informed decision-making based on accurate and accessible information. The position operates at the intersection of business needs, data architecture, and advanced analytics to ensure data flows are seamless and can be leveraged to derive actionable intelligence.

This role is based in Virginia and is available to Richmond or Lynchburg, VA hybrid in-office applicants or remote applicants residing in states/locations under Eastern or Central Time. States:
Alabama, Arkansas, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Virginia, Washington DC, Vermont, West Virginia, Wisconsin.

This role is not eligible for employment visa sponsorship.

• Data Modeling and Design:
  Develop conceptual, logical, and physical data models for business intelligence, analytics, and reporting solutions. Transform requirements into scalable, flexible, and efficient data structures to support advanced analytics.
• Requirement Analysis:
  Collaborate with business analysts, stakeholders, and subject matter experts to gather and interpret requirements for data initiatives. Translate business questions into data models that answer these questions.
• Data Integration:
  Work with data engineers to integrate data from multiple sources, ensuring consistency, accuracy, and reliability. Map data flows and document relationships between datasets.
• Database Architecture:
  Design and optimize database schemas using the medallion architecture, including relational, star schema, and denormalized data sets for BI and ML data consumers.
• Metadata Management:
  Collaborate with the data governance team to document data definitions, data lineage, and data quality statistics for data consumers.
• Data Quality Assurance:
  Establish master data management through data modeling so that the history of how customer, provider, and other party data are consolidated into a single version of the truth.
• Collaboration and Communication:
  Bridge between technical teams and business units, clearly communicating the value and limitations of data sources and structures.
• Continuous Improvement:
  Stay current with emerging trends in data modeling, analytics platforms, and big data technologies. Recommend enhancements to existing data models and approaches.
• Performance Optimization:
  Monitor and optimize data models for query performance and scalability. Troubleshoot and resolve performance bottlenecks with DBAs as needed.
• Governance and Compliance:
  Ensure data models and processes adhere to regulatory standards and organizational policies regarding privacy, access, and security.

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Mathematics, or a related field. Master’s degree preferred.
• Minimum 3 years of experience in data modeling, data analysis, or database development, preferably in analytics-driven environments.
• Proficiency in Erwin.
• Solid understanding of Databricks Delta tables and Postgre
  
  SQL.
• Experience with big data technologies and platforms (e.g., Databricks, Spark, AWS, Azure).
• Expertise in SQL.
• Strong analytical, problem-solving, and critical thinking skills.
• Excellent communication and interpersonal abilities.
• Experience with data governance, quality frameworks, and metadata management.
• Knowledge of business intelligence tools (e.g., Power BI, Spotfire).

• Data domain expertise in insurance and/or medical businesses.
• Knowledge of cloud data management, data lakes, and ETL processes.
• Ability to work independently and collaboratively in a cross-functional team environment.
• Strong attention to detail and commitment to data accuracy.
• Familiarity with Agile methodologies and project management practices.
• Experience with Python.
• Und…