Lead Data Modeler

DHL (Supply Chain)•Westerville, OH

55d•Onsite

About The Position

The Lead Data Modeler will have oversight regarding the design, creation, and maintenance of data models for a divisional and regional data warehouse in the Microsoft Azure cloud while collaborating with Global counterparts. This role will also be responsible for reviewing and recommending changes to documentation including naming convention, design pattern documentation, data product technical specifications, and data mapping documents. This role requires a strong understanding of data modeling concepts, cloud data architecture, and the ability to lead collaboration with various stakeholders to ensure data integrity and usability. This role may require leading data modelers at various skill levels.

Requirements

Bachelor’s degree (relevant field such as Computer Science, Information Technology, Data Science, or a related discipline desired) or equivalent experience
3 years data modeling experience for a regional or divisional data warehouse (focus on leadership and strategic initiatives), required
Experience with supply chain warehouse management systems (WMS) data, required
Data Profiling Experience, required

Responsibilities

Lead and mentor data modelers, providing guidance and support in their professional development.
Foster a collaborative team environment that encourages innovation and knowledge sharing.
Utilize effective communication and interpersonal skills to persuade stakeholders to adopt recommended data modeling practices and strategies.
Lead collaboration with data architects, business users, and business analytic leaders to gather and analyze data requirements, as derived from analytic product requirements. Collaboration may include resources working through vendor partner.
Review and recommend changes to conceptual and logical operational data models aligning with business processes to ensure accuracy and clarity.
Review logical and physical data models for the divisional and regional data warehouse to ensure alignment to requirements as well as compliance with established standards, naming convention, and best practices.
Review and recommend changes to documentation including naming convention, design pattern documentation, data product technical specifications, data dictionaries, and data mapping documents.
Review and recommend changes to business metadata for data models under purview of the data and analytics team.
Review monthly reconciliation report to understand differences between data model, DDL scripts and physical data warehouse. Leads collaboration with data architect, data engineers, and ETL developers (if necessary) to deploy changes to reconcile differences.
Stay updated with industry trends and best practices in data modeling, cloud architecture, and data warehousing.
Drive continuous improvement initiatives within the data modeling team to enhance efficiency and effectiveness.