Department Digital & Technology Office
Employee type Regular
Cebu Pacific is always up for new challenges, enabling our teams to stay at the forefront of agile and technological innovations. Pursuing a career under Information Technology at Cebu Pacific will enable you to play a pivotal role in shaping and securing the airline’s digital landscape from business analytics, system architecture, technology engineering, project management, cybersecurity, and cloud solutions. At Cebu Pacific, we build more than just technology—we create solutions that fuel the future of travel.
Bring your technical expertise to the team and be a moment maker in the ever evolving field of Information Technology as an Analytics Engineer.
The Analytics Engineer (Data Domain Architect) is responsible for designing, building, and maintaining enterprise data products that support analytics, reporting, self-service BI, and AI initiatives across Cebu Pacific. The role translates business requirements provided by Data Product Managers into trusted, scalable, and reusable data products through conceptual, logical, dimensional, and semantic data modeling. The Analytics Engineer owns domain data models, business definitions, semantic layers, metadata, lineage requirements, and analytics-ready datasets. Working closely with Data Engineers, AI Engineers, Data Governance, and business stakeholders, the Analytics Engineer ensures that data products are governed, documented, high quality, and aligned with enterprise standards and industry models. The Analytics Engineer is accountable for how data products are designed and built, ensuring they can be effectively reused across analytics, operational reporting, and AI use cases.
Primary Responsibilities
Data Modeling & Domain Architecture
- Design and maintain conceptual, logical, dimensional, and semantic data models for assigned business domains.
- Develop scalable and reusable domain architectures that support analytics, reporting, and AI use cases.
- Translate business requirements into data structures, entity relationships, hierarchies, and business rules.
- Align data models with enterprise architecture standards and industry frameworks.
- Ensure consistency of business definitions and domain structures across data products.
Semantic Layer & Metrics Management
- Design and maintain enterprise semantic models, metric layers, and reusable KPI frameworks.
- Define calculations, measures, hierarchies, and business logic supporting analytical consumption.
- Ensure consistent implementation of business definitions across reports, dashboards, and AI solutions.
- Manage semantic model documentation, versioning, and certification processes.
Data Product Engineering
- Design and build analytics-ready data products for self-service analytics, reporting, and AI consumption.
- Define reusable transformations, aggregations, and analytical structures.
- Collaborate with Data Engineers to ensure physical implementations align with business and semantic requirements.
- Support the development of certified enterprise datasets and reusable data assets.
- Drive reuse and standardization across multiple domains and use cases.
Metadata, Lineage & Documentation
- Maintain business and technical metadata for assigned data products.
- Define and document data product lineage and traceability requirements.
- Ensure data products are cataloged, documented, and discoverable.
- Support impact assessments and change management for data model modifications.
- Maintain data product documentation and data dictionaries.
Data Quality by Design
- Define quality requirements, validation rules, and business controls within data products.
- Collaborate with Data Governance and Data Quality teams to implement quality standards.
- Support root cause analysis and remediation of data quality issues.
- Ensure consistency, completeness, and accuracy of certification-ready datasets.
AI & Advanced Analytics Enablement
- Design datasets suitable for AI, machine learning, and advanced analytics applications.
- Partner with AI Engineers and Data Scientists to ensure feature consistency and data reuse.
- Support development of governed and reusable AI-ready data products.
Promote common analytical structures and enterprise-wide reuse of trusted data assets.
Qualifications
- Bachelor's Degree in Computer Science, Information Systems, Information Technology, Data Analytics, Engineering, Mathematics, Statistics, or a related field.
- Preferred: Professional certifications in Data Analytics, Data Engineering, Cloud Platforms, Data Management, or Enterprise Architecture (Microsoft, AWS, Azure, GCP, DAMA, TOGAF).
- Preferred: Formal training in Data Modeling, Data Warehousing, Analytics Engineering, Data Governance, or Metadata Management.
Technical & Data Skills
- Strong knowledge of data modeling, including conceptual, logical, dimensional, and semantic modeling techniques.
- Experience designing and managing enterprise data products, business metrics, and semantic layers.
- Proficiency in SQL and data analysis techniques for data validation, profiling, and analytical problem-solving.
- Experience with metadata management, data cataloging, data lineage, and data quality practices.
- Knowledge of modern analytics platforms, including Microsoft Fabric, Power BI, and related cloud data technologies.
- Familiarity with data governance, enterprise data standards, and information architecture principles.
- Preferred: Experience with Python for data analysis, data transformation, automation, analytics engineering, and AI/ML enablement.
Business & Functional Skills
- Ability to translate business requirements into scalable, reusable, and analytics-ready data products.
- Strong understanding of business processes, KPI frameworks, and decision-support requirements.
- Experience in data product development, documentation, and knowledge management.
- Ability to balance business needs, technical constraints, and enterprise architecture standards.
- Strong analytical and structured problem-solving skills.
Communication & Collaboration Skills
- Excellent stakeholder management and cross-functional collaboration skills.
- Ability to communicate complex data concepts to both technical and non-technical audiences.
- Strong facilitation, documentation, and requirements interpretation capabilities.
- Ability to work effectively with Data Product Managers, Data Engineers, AI Engineers, Governance teams, and business stakeholders.
Leadership Competencies
- Ownership and accountability for data product quality and delivery outcomes.
- Strong attention to detail and commitment to data accuracy and consistency.
- Continuous improvement mindset with a focus on standardization, reuse, and operational excellence.
- Learning agility and willingness to adopt new technologies, tools, and methodologies.
- Effective communication, collaboration, and influence across teams and stakeholders.
Why Join Us
- We are the first Great Place to Work certified airline in Southeast Asia.
- We have been recognized as Best Employer Brand on LinkedIn for two consecutive years.
- Be part of a forward-thinking team that values innovation and continuous improvement.
- Play a key role in developing and nurturing the talents that drive our success.
- Accelerate your career with access to extensive learning programs and leadership development initiatives, all under Ceb U, our corporate university.
- Enjoy unique employee perks such as free travel for you and your family. Expanded coverage to common law partners and same sex partners!
- Be assured of a comprehensive healthcare coverage upon hire.
Note: This position is for an Individual Contributor and will be based in Pasay City, Metro Manila but currently follows a hybrid workplace flexibility arrangement.
Your moment matters. Be a Moment Maker!
Experience Range (Years)
2 - 5 years