A data modeller will be responsible for designing, implementing, and maintaining data structures to support organizational needs. They create logical, conceptual, and physical data models, ensuring data is organized and accessible for business intelligence, analytics, and operational efficiency.
key responsibilities
* develop conceptual, logical, and physical data models to represent business processes and data relationships.
* define and document data modeling standards, conventions, and best practices.
* collaborate with stakeholders to gather and understand data requirements.
* create and maintain data dictionaries and schemas.
* define data storage and retrieval mechanisms optimized for performance and scalability.
* ensure data models align with the organization’s data architecture strategy.
* collaboration and communication
o work closely with database administrators, data engineers, business analysts, and developers.
o translate complex business requirements into efficient database structures.
o provide guidance on data integration and data quality best practices.
* analysis and optimization
o analyze existing data models and recommend improvements or enhancements.
o optimize databases for performance and efficiency.
o ensure data security, integrity, and compliance with regulatory requirements.
* documentation and maintenance
o maintain comprehensive documentation of data models and associated metadata.
o update models as business requirements evolve.
o provide training and support for teams using the data models.
skills and qualifications
* technical skills:
o proficiency in data modeling tools (e.g., erwin, powerdesigner, or lucidchart).
o strong knowledge of database systems (e.g., sql server, oracle, postgresql, mysql).
o experience with cloud databases ( snowflake preferred ).
o familiarity with big data technologies and nosql databases (e.g., mongodb, cassandra).
o visualisation skills: tableau sigma
o familiarity with dbt, fivetran & aws
* analytical skills:
o strong problem-solving and analytical thinking.
o ability to identify and mitigate potential data issues.
* communication skills:
o clear communication of technical concepts to non-technical stakeholders.
o collaborative approach to working across teams.
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