Senior Data Modeller at Interswitch

About the job

• Design, implement, and maintain robust data models that serve as the foundation for analytics, reporting, and machine learning, ensuring accuracy, consistency, and efficiency across the MVNE platform

RESPONSIBILITIES

Data Modelling & Architecture

• Develop and maintain logical and physical data models to organise data effectively for analytics, reporting, and machine learning across the MVNE.
• Optimize and enhance data models to improve query performance, simplify reporting, and support advanced analytics.

Collaboration & Stakeholder Engagement

• Collaborate closely with Data Engineers, Analysts, and tenant stakeholders to ensure data models meet evolving business requirements and regulatory standards.
• Work with stakeholders to identify high-value machine learning use cases (e.g., churn, credit scoring, dynamic pricing, network anomaly detection).

Standards & Best Practices

• Establish data modelling standards and best practices to ensure data integrity, consistency, and reusability across the platform.

Governance & Documentation

• Govern data models through robust documentation, version control, and proactive lifecycle management.

EDUCATION

General Education

• Bachelor's or master’s degree in Computer Science, Data Science, Applied Mathematics, or a related quantitative field.
• Certifications in Data Modelling, Database Management, or Analytics Engineering (e.g., DAMA, CDMP, dbt Certification) are advantageous.

EXPERIENCE

General Experience

• Minimum 7 years in data modelling, analytics engineering, or related roles; at least 5 years in the telecom industry.

Managerial Experience

• Minimum 5 years leading data‑science projects or small teams.
• Deep expertise in logical, physical, and dimensional data modelling using tools
• Expertise in Python, PySpark/SparkML, TensorFlow/PyTorch, and SQL.
• Hands-on experience with distributed data processing and lakehouse architectures.
• Familiarity with analytics workflows, including reporting, business intelligence, and supporting ML use-cases.
• Deep understanding of statistical modelling, causal inference, and experiment design.
• Familiarity with privacy-preserving and responsible AI techniques and Nigerian data protection regulations.