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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Inventory Manager Role - **Company:** Open Data Watch, Inc. - **Location:** Washington, DC, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Software Documentation, Data Discovery, Enterprise Information Management, Metadata, Metadata Repositories, Open Data Protocol, DataOps, Data Streaming, Enterprise Data Management, Data Management - **Published:** August 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ed6d5e35d00df7bb ## About the Role * Experience with data inventories, asset management, data catalogs, records, or enterprise information management. * Ability to design structured discovery, registration, reconciliation, and maintenance processes. * Working knowledge of ownership, metadata, classification, data quality, and lifecycle concepts. * Experience using inventory or catalog tools and interpreting system documentation and data flows. * Strong stakeholder coordination and reporting skills for managing completeness and remediation. About OPEN Data Jobs ## Description Data Inventory Managers establish and maintain the organization's account of its data assets. They define how assets are identified, registered, assigned an owner, classified for management, and reviewed for completeness, then coordinate the discovery work that keeps the inventory connected to real systems and programs. The role combines program management, data discovery, and persistent follow-through. Inventory Managers work with data owners, system teams, metadata specialists, and governance leaders to close coverage gaps, reconcile duplicate or stale entries, and report whether the inventory remains current enough to support mission questions and required reporting. They own enterprise coverage rather than the detailed metadata standard or daily data operations. What you'll build * Enterprise data-inventory structures, registration criteria, and asset-identification workflows. * Discovery approaches using surveys, system reviews, interviews, and technical scanning where appropriate. * Ownership maps linking data assets to accountable programs, systems, and stewards. * Completeness, currency, duplicate, and gap metrics that direct remediation work. * Inventory records for AI systems, models, datasets, and dependencies when the organization's governance approach requires them. Who you are You are persistent about coverage and ownership. You know an inventory is useful only when it reflects the actual environment, so you test assumptions, resolve ambiguities, and maintain a clear record of what is known and what needs follow-up. You can coordinate across a large organization without losing the detail. You create practical discovery routines and reporting that help technical teams and leadership see the same data estate., Federal openings may require familiarity with enterprise data inventories and agency metadata publication practices. Federal guidance describes an enterprise inventory as accounting for data assets across programs and bureaus, with metadata sufficient to support discovery and collaboration., Some roles may concentrate on open-data inventories, internal data assets, contract-delivered data, or artificial intelligence system inventories. The scope must be explicit: an inventory can point to an asset and its owner, but it is not a substitute for the detailed metadata, governance policy, or operational management of that asset. ## Related Videos - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [3 Ways to Rebuild the Data Stack for Agents](https://www.wearedevelopers.com/videos/100091-3-ways-to-rebuild-the-data-stack-for-agents) - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [Building an AI-Ready Content Lake: Scaling RAG and Document AI Beyond Demos](https://www.wearedevelopers.com/videos/1977-building-an-ai-ready-content-lake-scaling-rag-and-document-ai-beyond-demos) - [Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization](https://www.wearedevelopers.com/videos/253-data-fabric-in-action-how-to-enhance-a-stock-trading-app-with-ml-and-data-virtualization) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [With AIs wide open - WeAreDevelopers at All Things Open 2025](https://www.wearedevelopers.com/magazine/641-with-ais-wide-open-wearedevelopers-at-all-things-open-2025) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [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)