Analytics Data Modeler

At Genworth, we empower families to navigate the aging journey with confidence. We are compassionate, experienced allies for those navigating care with guidance, products, and services that meet families where they are. Further, we are the spouses, children, siblings, friends, and neighbors of those that need care—and we bring those experiences with us to work in serving our millions of policyholders each day.

We apply that same compassion and empathy as we work with each other and our local communities. Genworth values all perspectives, characteristics, and experiences so that employees can bring their full, authentic selves to work to help each other and our company succeed. We celebrate our diversity and understand that being intentional about inclusion is the only way to create a sense of belonging for all associates. We also invest in the vitality of our local communities through grants from the Genworth Foundation, event sponsorships, and employee volunteerism.

Our four values guide our strategy, our decisions, and our interactions:
• Make it human. We care about the people that make up our customers, colleagues, and communities.
• Make it about others. We do what's best for our customers and collaborate to drive progress.
• Make it happen. We work with intention toward a common purpose and forge ways forward together.
• Make it better. We create fulfilling purpose-driven careers by learning from the world and each other.

POSITION TITLE
Analytics Data Modeler Lead

LOCATION
This position is available to Virginia residents as Richmond or Lynchburg, VA Hybrid in-office applicants or remote applicants residing in states/locations under Eastern or Central Standard Time: 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 or Wisconsin.

This role is not eligible for employment visa sponsorship.

YOUR ROLE
The Analytics Data Modeler plays a vital role in transforming raw data into meaningful business insights, which is essential for unlocking the value of data within organizations. This professional is responsible for designing, developing, and maintaining robust data models that empower organizations to make informed decisions based on accurate and accessible information. Working at the intersection of business needs, data architecture, and advanced analytics, the Analytics Data Modeler ensures that data flows seamlessly and can be leveraged to derive actionable intelligence to empower teams to uncover insights, drive strategy, and achieve business success in the data-driven age..

What you will be doing
• 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 that can support advanced analytics.
• Requirement Analysis: Collaborate with business analysts, stakeholders, and subject matter experts to gather and interpret requirements for new data initiatives. Translate business questions into data models that can answer these questions.
• Data Integration: Work closely 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 which includes relational, star schema and denormalized data sets for BI and ML data consumers.
• Metadata Management: Team with the data governance team so detailed documentation on data definitions, data lineage, and data quality statistics are available to data consumers.
• Data Quality Assurance: Establish master data management and data modeling practices that preserve historical context, explain data changes resulting from remediation or repairs, and enable consumers to understand variances from source systems.
• Collaboration and Communication: Serve as a bridge between technical teams and business units, clearly communicating the value and limitations of various data sources and structures.
• Continuous Improvement: Stay abreast of emerging trends in data modeling, analytics platforms, and big data technologies. Recommend enhan