Data Modeler

Role Title:Data Modeler

Location : Remote - USA 

Role Description

This person will be in a client-facing role on a team comprised of a Project Manager, Senior Enterprise Architect, and Business Process Consultant and senior project leadership. Strong technical data modeling and client-facing skills are required.

The Data Modeler will primarily be responsible for analyzing current state data models from 2019 and working to create updated current state enterprise data models and supporting data management artifacts. Key tasks include creating conceptual data model diagrams and logical data model diagrams to reflect PBGC’s current state. This will require understanding current systems, system integrations, data domains/information types/entities, and relationships between systems and data across the enterprise (including data domains such as Pension Plan Management, Financial Management, HR, Legal, IT and others). Data models will be created in the client environment using available tools such as Microsoft Visio and/or PowerBI with limited allowable AI tool use (client provides access to M365 Copilot). The Data Modeler – alongside the core project team – will also advise on a refreshed enterprise data strategy, data categorization, data classification, common data mapping, integration/API strategy, API catalog standards, enabling technologies to support PBAI & Data goals, target architecture, and modernization plans/transition roadmaps for the agency.

This resource should be comfortable navigating complex enterprise IT ecosystems/data models and advising on best practices for data categorization, data classification, data modeling (conceptual, logical, physical and other types of data models). Client’s technical environment is complex (multi-cloud, mix of SaaS / COTS / Customs systems, 50+ systems with many integrations). The agency has recently begun their AI adoption journey by building an AI Governance Council, AI use case catalog, and a few pilot projects – but this Enterprise Data Analysis & Support project will shape the enterprise-level target architecture and data strategy to support AI. This may include advising on an integration/API strategy, use cases for data warehouses/datalakes, and a master data management strategy.

Data quality analysis, physical data model analysis, system redesign, data cleansing, data quality improvements, and AI/ML solution development/implementation are out of scope.