Role Description:
An enterprise Data Modeler to design, develop, and optimize data models that support our organisations data architecture and business objectives. The ideal candidate will have expertise in design of enterprise data models and strategies for organizing data at scale.
Our ideal candidate will have
* 7+ Years experience in data modelling and developing enterprise-wide data strategies
* Exposure to petabyte scale data sets
* Undergraduate in Computing or Information Systems or related field
* Post graduate qualification highly desirable
* Highly collaborative working style
1. Technical Competencies
* Data Modelling Techniques - Expertise in conceptual, logical, and physical data modeling using tools like ERwin, PowerDesigner, or Visio.
* Database Management – Deep knowledge of relational (SQL) and NoSQL databases such as PostgreSQL, MySQL, MongoDB, and Cassandra.
* Data Warehousing & ETL – Experience with data warehousing solutions (e.g., Databricks, Snowflake, Redshift, BigQuery) and ETL processes.
* Data & Cloud Platforms – Familiarity with cloud-based data solutions (AWS, Azure, GCP) and big data technologies (Hadoop, Spark).
* Data Governance & Security – Understanding of data privacy, compliance (US Healthcare, HIPAA, etc), encryption, and role-based access control.
* Metadata & Master Data Management (MDM) – Experience in structuring and maintaining high-quality metadata and master data.
* MongoDB Modelling Techniques –Experience modelling data for mongodb, embedding versus references, when and why
* Advanced Data Modelling - Understanding of advanced data modelling topics such as semantic modelling and knowledge graphs
2. Analytical & Problem-Solving Skills
* Data Analysis & Interpretation – Ability to assess data quality, identify inconsistencies, and propose optimization strategies.
* Performance Optimization – Skills in indexing, partitioning, and query tuning to enhance database efficiency.
* Business & Domain Knowledge – Understanding industry-specific data requirements (e.g., finance, healthcare, e-commerce).
* Troubleshooting & Debugging – Ability to diagnose issues within complex data pipelines and architectures.
3. Strategic & Design Thinking
* Enterprise Data Strategy – Ability to align data architecture with business goals and long-term scalability.
* Data Lifecycle Management – Designing for efficient data collection, storage, retrieval, and retirement.
* Data Interoperability & Standardization – Ensuring consistent data definitions across different systems.
* Modeling for AI/ML – Structuring data to support analytics, business intelligence (BI), and machine learning applications.
4. Adaptability & Continuous Learning
* Keeping Up with Trends – Staying updated with emerging database technologies, modeling techniques, and regulations.
* Agile & DevOps Methodologies – Working in fast-paced, iterative development environments.
* Experimentation & Innovation – Willingness to test new data architectures, tools, and optimization strategies.
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