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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Architect - Investment Data & Analytics - **Company:** AI Enabled Solutions LLC - **Location:** Nashville, TN, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Business Analytics Applications, Data Analysis, Cloud Database, Information Systems, Data Architecture, Data Dictionary, Data Governance, Data Infrastructure, Data Security, Data Structures, Data Warehousing, Software Design Patterns, Dimensional Modeling, Information Lifecycle Management, Information Management, Knowledge Management, Metadata, Meta-Data Management, Performance Tuning, Power BI, Cloud Services, Data Streaming, Enterprise Data Management, Sql Optimization, Snowflake, Generative AI, Data Strategy, Information Technology, Data Lineage, Data Analytics, Data Management, Tools for Reporting, Semantic Modeling, Domain Driven Design, Natural Language Understanding - **Published:** October 2, 2026 - **Apply:** https://www.dice.com/job-detail/5b6fa45e-7827-4d2e-bbf3-2971d245f7f5 ## About the Role * Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or related discipline. * Master's degree preferred. Experience * 10+ years of experience in data architecture, data modeling, or enterprise data management. * Significant experience designing analytical data platforms and data warehouses. * Strong experience with Snowflake and cloud-based data ecosystems. * Experience within financial services, asset management, or investment technology preferred. Technical Skills Data Architecture * Enterprise data architecture frameworks and best practices. * Logical, conceptual, and physical data modeling. * Domain-driven design and data product architecture. * Data lifecycle and information management practices. Snowflake & Modern Data Platforms * Snowflake architecture and performance optimization. * Advanced SQL and analytical modeling techniques. * Data warehouse and lakehouse architecture patterns. * Experience with cloud data platforms and modern analytics ecosystems. Analytics & Semantic Modeling * Dimensional modeling (star and snowflake schemas). * Semantic layer design and business-friendly data abstractions. * Power BI and analytics platform integration. * Self-service analytics enablement. Governance & Metadata * Data lineage, metadata management, and business glossaries. * Microsoft Purview or equivalent governance platforms. * Data quality frameworks and validation methodologies. * Access controls and governed data consumption patterns. AI & Emerging Technologies * Understanding of AI-enabled analytics and natural language data access. * Knowledge of semantic modeling and retrieval architectures. * Experience supporting AI-ready data ecosystems and knowledge management strategies. * Familiarity with GenAI, agent-based solutions, and data discoverability concepts. ## Description The Data Architect will lead the design, governance, and evolution of the firm's investment data architecture, enabling scalable analytics, reporting, AI-driven insights, and self-service data consumption. This role serves as the bridge between business, investment stakeholders, and technology teams, ensuring that data is modeled, governed, documented, and structured to support modern investment processes and future AI-enabled capabilities. The role combines deep expertise in data architecture, Snowflake modeling, semantic layer design, metadata management, governance, and investment data domains., * Define and maintain target-state architecture for investment data platforms and analytical ecosystems. * Design logical, physical, and conceptual data models that support portfolio management, research, trading, risk, performance, and attribution analytics. * Establish architecture standards, design patterns, and best practices across investment technology. * Drive domain-based architecture and data product strategies aligned with enterprise objectives. Data Modeling & Snowflake Architecture * Architect and maintain scalable Snowflake data structures optimized for analytics and AI consumption. * Design dimensional, domain-oriented, and consumption-focused data models. * Develop and govern semantic layers, curated views, and reusable analytical data products. * Ensure data models support reporting platforms, self-service analytics, and downstream applications. Data Governance & Quality * Define enterprise standards for metadata, lineage, data definitions, and business glossaries. * Establish data quality frameworks, reconciliation controls, and validation processes. * Partner with governance, security, and compliance teams to ensure controlled and governed data access. * Promote adoption of enterprise data catalogs and metadata management practices. AI-Ready Data Foundations * Architect data structures that support natural language querying, AI-assisted analytics, and generative AI solutions. * Define semantic frameworks that make business and technical context discoverable and reusable. * Ensure data and metadata are structured for AI, agent-based access, and future intelligent applications. * Partner with AI and analytics teams to enable contextual, cross-domain data experiences. Documentation & Knowledge Management * Establish standards for documenting data architecture, models, lineage, business definitions, and usage patterns. * Create and maintain architecture diagrams, data flow documentation, data dictionaries, and technical specifications. * Ensure architecture artifacts remain accurate, discoverable, and aligned with evolving business processes. * Champion documentation practices that support both human users and AI-enabled knowledge retrieval. Stakeholder & Business Partnership * Collaborate with portfolio managers, researchers, risk teams, operations, and technology partners to understand data requirements. * Translate business objectives into scalable architectural solutions. * Lead architecture reviews and provide guidance on technology and data strategy decisions. * Communicate complex architectural concepts effectively to both technical and non-technical audiences. ## Related Videos - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [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) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Beyond SQL Generation: How to Teach Agents What Your Database Actually Means](https://www.wearedevelopers.com/videos/100127-beyond-sql-generation-how-to-teach-agents-what-your-database-actually-means) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [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) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Got AI ideas but no money? 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