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Data Architect

1 week ago

Melbourne, Victoria, Australia The Vanguard Group Full time

Vanguard's Technology division is at the forefront of innovation, driving growth and success for our clients and crew. As a Data Modeler, you will play a critical role in transforming how our team uses data to inform every decision.

The CDAO team is responsible for establishing technical excellence by understanding what our critical data is, where it is, and how it's being used. This supports our reporting suite, global compliance, data quality efforts, and data security efforts.

As our Data Modeler, you will lead the development and rollout of physical, logical, and conceptual Entity Relationship Diagrams with a clear focus on documenting our current landscape of data sources as well as our to-be Data Lakehouse infrastructure.

You'll define the standards for our new data store and guide the Engineering team to build a modern solution that enables self-serve data. The Data Engineering team is a full stack team that includes Product Owners, Scrum Masters, Data Engineers, Tech Leads, and Architects.

Data Modelling Responsibilities

  1. Explore our data sources to understand, model, and document the data we take from them.
  2. Build Entity Relationship Diagrams to show the physical, logical, and conceptual makeup of our holistic data sources and our target databases.
  3. Work closely with people across all aspects of Vanguard's business to understand data coming into our team and how data will be used once provided by our team.
  4. Develop queries and other test artifacts to validate data is appropriately flowing through data pipelines in line with business requirements.
  5. Deliver training and knowledge share sessions to the Data Engineering team covering the designed data models.
  6. Liaise with our Product Owner, Data Analyst, Data Scientists, Product Architect, Data Engineers and engage business stakeholders directly to validate assumptions and design decisions.
  7. Participate in design, code, and test inspections throughout the life cycle to identify issues and improvements to our data models.