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Data & Analytics Engineer at AIICO Insurance
AIICO Insurance
October 13, 2026
Full-time
On-site
Job Summary
The Data & Analytics Engineering function is responsible for building and maintaining the data pipelines, models, and platforms that power the Enterprise Data Warehouse (EDW) and business intelligence initiatives.
This role combines data engineering and analytics engineering to ensure that high-quality, well-modeled, and timely data is available for reporting, advanced analytics, and actuarial IFRS17 Reporting.
Responsibilities
Data Engineering:
Maintain the Enterprise Data Warehouse (EDW) as a trusted single source of truth to support analytics, reporting, and advanced modeling.
Design, build, and optimize ETL/ELT pipelines using Python, dbt, and Airflow for orchestration.
Collect and load structured and unstructured data from internal and external systems.
Manage the organization-wide data collection process for IFRS17, automating inputs from data stewards and producing monthly outputs for actuarial evaluations.
Design and optimize dbt models, transforming raw data into analytics-ready datasets.
Ensure data quality, accuracy, and integrity across all pipelines and reporting systems.
Analyze the organization's data maturity level, uncover gaps, and recommend improvements.
Continually assess data quality, clean dirty data and raise concerns over gaps with closure recommendations
Implement best practices for data governance, model testing, validation, and documentation.
Ensure proper version control and CI/CD workflows using Git and Azure DevOps.
Collaborate with analysts, actuaries, and business stakeholders to deliver actionable insights.
Uphold data security and governance in collaboration with DBAs and system administrators.
Assess new and existing data systems and continually uncover gaps to improve data maturity
Analytics Engineering:
Develop, maintain, and optimize dbt models with tests, documentation, and version control.
Translate business requirements into scalable data models that serve analytics and reporting needs.
Support incremental data refreshes transformation logic to improve automation and scalability.
Continuously monitor and troubleshoot data workflows within the warehouse for improvement.
Create Machine learning models for predictive analytics
Collaborate with business units to define KPIs, use cases, and reporting needs.
Support the development of BI solutions using SSRS, SSIS, and SSAS.
Enable Data Analysts and business stakeholders by providing well-structured, analytics-ready datasets.
Provide technical guidance for predictive analytics and advanced statistical modeling.
Supply Data models and insightful knowledge base to the AI system.
Requirements
HND / B.Sc Degree in Computer Science or any related discipline.
Knowledge of SQL & Python is an added advantage
Minimum of 3 - 4 years experience.
The Data & Analytics Engineering function is responsible for building and maintaining the data pipelines, models, and platforms that power the Enterprise Data Warehouse (EDW) and business intelligence initiatives.
This role combines data engineering and analytics engineering to ensure that high-quality, well-modeled, and timely data is available for reporting, advanced analytics, and actuarial IFRS17 Reporting.
Responsibilities
Data Engineering:
Maintain the Enterprise Data Warehouse (EDW) as a trusted single source of truth to support analytics, reporting, and advanced modeling.
Design, build, and optimize ETL/ELT pipelines using Python, dbt, and Airflow for orchestration.
Collect and load structured and unstructured data from internal and external systems.
Manage the organization-wide data collection process for IFRS17, automating inputs from data stewards and producing monthly outputs for actuarial evaluations.
Design and optimize dbt models, transforming raw data into analytics-ready datasets.
Ensure data quality, accuracy, and integrity across all pipelines and reporting systems.
Analyze the organization's data maturity level, uncover gaps, and recommend improvements.
Continually assess data quality, clean dirty data and raise concerns over gaps with closure recommendations
Implement best practices for data governance, model testing, validation, and documentation.
Ensure proper version control and CI/CD workflows using Git and Azure DevOps.
Collaborate with analysts, actuaries, and business stakeholders to deliver actionable insights.
Uphold data security and governance in collaboration with DBAs and system administrators.
Assess new and existing data systems and continually uncover gaps to improve data maturity
Analytics Engineering:
Develop, maintain, and optimize dbt models with tests, documentation, and version control.
Translate business requirements into scalable data models that serve analytics and reporting needs.
Support incremental data refreshes transformation logic to improve automation and scalability.
Continuously monitor and troubleshoot data workflows within the warehouse for improvement.
Create Machine learning models for predictive analytics
Collaborate with business units to define KPIs, use cases, and reporting needs.
Support the development of BI solutions using SSRS, SSIS, and SSAS.
Enable Data Analysts and business stakeholders by providing well-structured, analytics-ready datasets.
Provide technical guidance for predictive analytics and advanced statistical modeling.
Supply Data models and insightful knowledge base to the AI system.
Requirements
HND / B.Sc Degree in Computer Science or any related discipline.
Knowledge of SQL & Python is an added advantage
Minimum of 3 - 4 years experience.