> Markdown version of [/jobs/ext/2003331-lead-data-architect](https://www.wearedevelopers.com/jobs/ext/2003331-lead-data-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Architect - **Company:** Genworth Financial, Inc. - **Location:** Richmond, VA, United States - **Experience:** Expert - **Salary:** $120,900.0 - $187,000.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Microsoft Azure, Big Data, Information Systems, Data Architecture, Data Governance, Data Security, Data Systems, Database Development, Machine Learning, Meta-Data Management, Operational Data Store, Performance Tuning, Cloud Services, Apache Spark, Data Lakes, Information Technology, Data Management, Physical Data Models, Data Pipelines, Databricks - **Published:** August 9, 2026 - **Apply:** https://www.nettemps.com/it/en/W9A2002D866562F5457.ntap ## About the Role * Bachelor's degree in computer science, Information Systems, Data Science, Mathematics, or related field. Master's degree preferred. * Technical qualifications + Minimum 3 years of experience in data modeling in a Lakehouse analytics environment. + Proficiency in a data modeling tool such as ER Studio. + Experience with big data technologies and platforms (eg, DataBricks, Spark, AWS, Azure). + Expert level in DDL and SQL development + Experience working with data governance, quality frameworks, and metadata management, and how this connects to data modeling practices and needs. * Overall qualifications + Strong analytical, problem-solving, and critical thinking skills needed for data modeling + Excellent communication and interpersonal abilities. + Ability to work independently and collaboratively in a cross-functional team environment. Preferred Skills * Understanding of PII and PHI data and how they are governed and secured * Experience working on an agile team using agile tools, and associated practices. * Data taxonomies in insurance * Knowledge of Azure and AWS services relevant to data architecture and services * Knowledge of data lakes, and data pipelines that transform raw data into the data that is required, * Business Acumen: Ability to understand insurance business processes, goals, and pain points to design data models that solve real-world problems. * Technical Expertise: Deep familiarity with modern data modeling techniques, database management, and analytics platforms. * Adaptability: Ability to thrive in a fast-paced, dynamic environment and quickly pivot between projects. ## Description You will be designing the structural blueprints for Genworth's data systems, ensuring that the data is secure, accessible, and available so that business goals and requirements are met with your blueprints. The blueprints will have enough detail on how data is collected, stored, integrated, transformed, and published so other teams can implement the blueprints. The blueprints you build will also contain capability frameworks so the how data is collected, stored, integrated, transformed, and published is done in a consistent manner across teams when appropriate and also with pre built components or templates to further enable a consistent approach and enable more cost effective and faster execution of data product delivery., * Establish enterprise data models including an enterprise insurance data model that is followed by different build and vendor partners. * Define data domains and structures based on business requirements. * Define and enable consistent data designs so data solution construction and data user experience is consistent. Create decision trees to define what must be consistent based on the situation and requirements. * Define and design interoperability across data solutions including the technology platforms, data pipelines, data models, ML and AI applications, data governance platforms, data security, and hyper scaler cloud services * Align and leverage data governance interoperability work such as leveraging data stewards for defining data model attribute names and definitions. Also, work with the data governance team on metadata information about the data model that is needed for data governance management. * Data modeling: Develop conceptual, logical, and physical data models for business intelligence, AI and ML requirements, and operational data use cases. * Performance Optimization: Monitor and optimize data models for query performance and scalability., * Collaborating with data modelers on how their data model scope should integrate into the enterprise insurance data model * Participating in meetings with business stakeholders to understand analytical or operational data needs so the best data modeling approach is selected. * Designing and documenting data models, including entity relationships and dimensional models. * Collaboration and Communication: Serve as a bridge between technical teams and business teams, clearly communicating the value and limitations of data models so adoption of the data models occurs. * Data model training activities so business users can effectively build required queries against the model. * Collaborating with data engineers to create data pipelines to populate the data models. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [Enabling intelligent logistics automation: home-grown Industrial IoT platform at Austrian Post](https://www.wearedevelopers.com/videos/2018-enabling-intelligent-logistics-automation-home-grown-industrial-iot-platform-at-austrian-post) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)