System Analyst

Nat Re - National Reinsurance Corporation of the Philippines; Makati; On-site; PHP 420,000 - 850,000; Full time

Job summary

Nat Re is seeking a data-focused professional to drive analysis, automation, and data integrity across financial and operational datasets. The role emphasizes advanced Excel, SQL, Python, and ETL/ELT capabilities to optimize workflows and reporting.

You will contribute to data governance, UAT activities, and documentation, while leveraging AI techniques to streamline repetitive tasks and improve decision support.

Qualifications

• Bachelor’s degree in a closely related technical field.
• Proficiency in Power Query, VBA/Macros, and advanced Excel features.
• Strong SQL and Python experience for data analysis and automation.
• Experience with ETL/ELT processes and data integration.

Responsibilities

• Process, validate, and reconcile large volumes of data.
• Perform root-cause analysis on data anomalies and reporting errors.
• Build and optimize data pipelines to ensure data integrity.
• Support maintenance and improvement of core systems and tools.
• Create end-to-end operational documentation and SOPs.
• Participate in UAT and system migrations.
• Apply AI and automation to streamline tasks.

Skills

Advanced Excel

MSSQL

Python

Databases

ETL/ELT

Git

Data Warehousing

Cloud Computing

Data Quality

CI/CD

Information Security

Data Governance

Data Architecture

Education

Bachelor's degree in Computer Science / Software Engineering / IT or related field

Job description

Nat Re provides life and non-life reinsurance capacity, and, in relation to this, consultancy, technical, and advisory services. We cater to independent insurers and multinationals operating in the Philippines, and to (re)insurers abroad who aim to diversify their portfolios. To know more about the company, please visit www.nat-re.com.

About the Role

The ideal candidate possesses exceptional data analysis skills, advanced Excel capabilities, and practical exposure to leveraging Artificial Intelligence (AI) tools to automate workflows, optimize operational efficiency, and drive insight delivery. Additionally, you bring hands-on experience with Extract, Transform, Load (ETL/ELT) processes to efficiently extract source data across disparate financial and policy systems, transform it to align with the system standards, and load it seamlessly into enterprise calculation engines and data repositories.

Responsibilities

Data Analysis & Operations

• Process, validate, and reconcile large volumes of financial and operational data.
• Perform root-cause analysis on data anomalies, contract group discrepancies, and operational reporting errors.
• Build, maintain, and optimize data pipelines to ensure data integrity and accurate reporting across systems.

Systems & Process Optimization

• Support the maintenance and continuous improvement of core operational systems and analytical tools.
• Create and update end-to-end operational documentation, data flow diagrams, and Standard Operating Procedures (SOPs).
• Participate in User Acceptance Testing (UAT), system migration efforts, and post-implementation reviews.
• Apply AI and automation techniques to streamline repetitive operational and data-processing tasks.

Required Skills & Qualifications

Core Requirements

• Education: Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science, Information Systems, or a closely related technical field.

Required Skills

• Advanced Excel: High proficiency in Power Query, VBA/Macros, complex formulas, dynamic arrays, and data modeling.
• MSSQL: Advanced proficiency in writing SQL queries to extract, transform, and analyze large datasets across relational databases.
• Python: Practical experience using Python for data analysis, scripting, and task automation.
• Databases: Hands-on experience working with and managing relational database systems (e.g., PostgreSQL, SQL Server, Oracle).
• ETL/ELT: Demonstrated experience designing, building, and executing data integration and transformation pipelines (ETL/ELT).
• Git: Experience using Git/GitHub/GitLab for version control and collaborative code management.
• Basic Knowledge Requirements (or nice to have)
• Data Warehousing & Data Lakes: Basic understanding of central data storage models, analytical database structures, and unstructured/semi-structured data repository concepts.
• Cloud Computing: Foundational familiarity with core cloud platforms, services, and cloud-hosted data environments.
• Data Quality: Fundamental understanding of data validation techniques, profiling, data cleansing standards, and integrity checks.
• CI/CD: Conceptual understanding of Continuous Integration and Continuous Deployment principles for automated testing and code deployment pipelines.
• Information Security: Basic awareness of data security controls, access management, data privacy principles, and compliance requirements.
• Data Governance: General understanding of data lineage, metadata management, ownership frameworks, and regulatory policies.
• Data Architecture: High-level grasp of system architecture design, data flow mapping, and enterprise data modeling principles.