Data Modelers should be able to design both relational, data warehousing, data mart, and non-relational database models to translate complex business data into data-driven applications. Experts in this TalentCloud should be able to work with data architects to design databases that meet organizational needs using conceptual, logical, and physical data models. Other abilities and experiences include:
- Design data models to improve efficiency and outputs, and may focus on issues such as reducing data redundancy or improving data movement across systems.
- Should be able to identify business needs and areas where data can be used to improve business activities, defining business users’ requirements.
- Using this understanding of data flows, they propose and implement innovative data-driven solutions backed by the appropriate data model
- Should be able to create and maintain Data Models as business and data requirements evolve and change
- Should be able to work closely with the database engineers to create optimal physical data models of datasets, create and maintain data maps and systems interrelationship diagrams for data domains and systems
- Define and set standards and govern data modeling and design tools, best practices, and related development methodologies for the organization
- Set standards for document naming, security, and lifecycle & retention architecture
- Make recommendations of tools, best practices, and proper data usage
- Should champion the usage of data in business, and communicate the benefits and return on investment for application and product owners
- Understand and translate business needs into data models supporting long-term solutions
- Work with the Application Development team to implement data strategies, build data flows and develop conceptual data models
- Create logical and physical data models using best practices to ensure high data quality and reduced redundancy
- Optimize and update logical and physical data models to support new and existing projects
- Enhance and maintain the different data models along with corresponding metadata
- Develop best practices for standard naming conventions and coding practices to ensure consistency of data models
- Recommend opportunities for reuse of data models in new environments.
- Perform reverse engineering of physical data models from databases and SQL scripts
- Evaluate data models and physical databases for variances and discrepancies
- Validate business data objects for accuracy and completeness
Required Skills
- Experience in data modeling
- Expertise in data modeling principles/methods including conceptual, logical & physical Data Models
- Expert level understanding of Normal Forms for Relational databases
- Expert level understanding of Star Schema and snowflake data models and create right models for aggregation and aggregate lattices and hierarchies
- Good level of understanding of Kimball and Inmon style Data Warehouse architectures and approaches
- Modeling with different NoSQL databases and their corresponding underlying models – Key-Value, Wide Column, Graph databases, and Document databases
- Ability to clearly communicate complex technical ideas, regardless of the technical capacity of the audience
- Strong interpersonal skills and ability to work as part of a team
- Ability to quickly learn and adapt modeling methods from case studies or other proven approaches
- Ability to utilize Business Intelligence tools (Power BI) to represent insights
- Experience working with dimensionally modeled data
- Experience in translating/mapping relational data models into JSON, BSON XML and Schemas, JSON-LD, etc.
- Data analysis and modeling tools (e.g. Power Designer, ERWin, ER/Studio)
- SQL and/or PL/SQL
- RDBMS platforms (e.g. SQL Server, Oracle, Netezza, Teradata, DB2 / UDB)
- Microsoft Excel, Word, PowerPoint, and Visio
Preferred Skills
- Ability to model the different approaches of Kimball – SCD (Slowly Changing Dimensions), Bridge Tables to handle multi-valued dimensional data
- Good Strong oral, written communication, and leadership skills
- Ability to handle multiple tasks and workstreams in a fast-paced environment
- SDLC methodologies (Waterfall and Agile Development)
