The data modeler designs, implements, and documents data architecture and data modeling solutions, which include the use of relational, dimensional, and NoSQL databases. These solutions support enterprise information management, business intelligence, machine learning, data science, and other business interests.​

In order to make an application, simply read through the following job description and make sure to attach relevant documents.

- Be responsible for the development of the conceptual, logical, and physical data models, the implementation of RDBMS, operational data store (ODS), data marts, and data lakes on target platforms (SQL/NoSQL).​

- Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices. The candidate must be able to work independently and collaboratively.​

Responsibilities​: -

- Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, and machine learning).​

- Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models​

- Define and govern data modelling and design standards, tools, best practices, and related development for enterprise data models.​

- Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization.​

- Hands-on modelling, design, configuration, installation, performance tuning, and sandbox POC.​

- Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.​

Skills​: -

- Bachelor’s or master’s degree in computer/data science technical or related experience.​

- 5+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, NoSQL data platform technologies, and ETL and data ingestion protocols).​

- Experience with data warehouse, data lake, and enterprise big data platforms in multi-data-centre contexts required.​

- Good knowledge of metadata management, data modelling, and related tools (Erwin or ER Studio or others) required.​

- Experience in team management, communication, and presentation.