Chief Data Infrastructure Architect
4 days ago
Overview:
Electronic Solutions is seeking a highly skilled Data Modelling and Strategy Lead to join our team. In this role, you will be responsible for leading the development of conceptual, logical, and physical data models.
Responsibilities:
- Data Modelling: Develop conceptual, logical, and physical data models to support business requirements.
- Data Architecture: Implement business and IT data requirements across relational, dimensional, and NoSQL platforms.
- Standards and Best Practices: Define and enforce data modelling and design standards, tools, and best practices.
- Data Infrastructure: Oversee the implementation of RDBMS, operational data store (ODS), data marts, and data lakes.
- Security and Analytics: Identify architecture, infrastructure, and interfaces to data sources, ensuring security and analytic model support.
- Query Performance: Optimize data query performance through best practices, identifying areas for improvement in current data systems.
- Collaboration: Collaborate with teams, addressing project requirements and reducing delivery risks.
- Data Tools: Support the development of data tools for reporting, visualization, analytics, and machine learning.
- Data Infrastructure Management: Design and manage data infrastructure to extract, organize, and store data for authorized users to access.
- Data Sources: Identify internal and external data sources within the organization, ensuring the accuracy, security, and accessibility of the data.
Desired Candidate Profile:
- Data Modelling Expertise: Strong expertise in conceptual, logical, and physical data modelling, in addition to data warehouse and data lake design approaches with hands-on experience of Inmon & Kimball methodologies.
- Technology Skills: Hands-on experience with RDBMS, NoSQL databases, and Cloudera CDP technologies.
- Leadership Experience: Proven experience in defining and implementing data architecture strategies.
- Communication Skills: Excellent communication skills for technical and non-technical stakeholders.
- Performance Tuning: Experience in performance tuning for data queries.
- Data Modelling Standards: Understanding of data modelling and design standards, tools, and best practices.
- Analytical Mindset: Analytical mind-set and problem-solving skills for proactive project support.
- Modelling Tools: Experience with modelling tools like ERWIN.
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