Data Architect Consultant

MediCard Phils., Inc. is one of the country's leading HMO and the only HMO founded and run by Doctors. Since its inception, the concept of service-oriented total health care has been the molding ideal of MediCard.  The competition is vast, and the benefits being offered by the competitors are tempting.  However, MEDICard has taken the lead in providing innovative and productive ideas that cut down the cost of health maintenance without compromising its quality.

MediCard now boasts of more than half a million members and over 54,000 accredited doctors in over 1,000 hospitals and clinics nationwide. It also operates 16 MediCard free-standing clinics that provide services at par with those offered by hospitals minus the confinement.

MediCard is currently looking for assertive, dynamic and energetic individuals to fill up the following vacancy:

A Data Architect Consultant is responsible for designing, governing, and optimizing the enterprise data architecture to support secure, reliable, scalable, and business-aligned data capabilities. The role defines data models, data flows, integration patterns, data platform designs, and governance standards across operational systems, analytics platforms, data warehouses, data lakes, and cloud-based data solutions. The Data Architect works closely with business stakeholders, data governance, analytics, application teams, infrastructure, security, and delivery teams to ensure data is properly structured, protected, integrated, documented, and prepared for reporting, analytics, regulatory, and digital transformation needs.1. Enterprise Data Architecture Strategy and Design
• Design and maintain the enterprise data architecture blueprint, covering data platforms, data domains, data flows, data models, integration points, and information lifecycle requirements.
• Align data architecture with business strategy, digital transformation priorities, enterprise architecture standards, regulatory requirements, and technology roadmaps.
• Define architecture options, trade-offs, risks, dependencies, and recommendations to support data-driven decision-making and platform modernization.
 
2. Data Modeling and Data Design
• Create conceptual, logical, and physical data models for operational systems, data warehouses, data marts, data lakes, lakehouses, and analytics platforms.
• Define data entities, relationships, standards, naming conventions, metadata, reference data, master data, and data classification requirements.
• Ensure data models support business reporting, analytics, interoperability, scalability, performance, auditability, and long-term maintainability.
 
3. Data Platform and Cloud Architecture
• Architect modern data platforms using cloud and hybrid technologies, including data lakes, data warehouses, lakehouse platforms, data integration services, analytics tools, and storage solutions.
• Apply architecture best practices for availability, security, scalability, performance, reliability, observability, cost optimization, and disaster recovery.
• Establish reusable data platform patterns and reference architectures to guide project delivery and promote consistency across data initiatives.
 
4. Data Integration, ETL/ELT, and Data Flow Design
• Design secure and reliable data integration patterns between source systems, enterprise platforms, cloud services, reporting tools, third-party providers, and partner ecosystems.
• Define ETL/ELT patterns, batch and real-time data flows, data ingestion standards, transformation rules, reconciliation controls, error handling, and monitoring requirements.
• Ensure data integration designs support data quality, completeness, traceability, lineage, auditability, operational reliability, and business continuity.
 
5. Data Governance, Quality, Security, and Compliance
• Embed data governance and security-by-design principles into data architecture, including data ownership, stewardship, classification, privacy, access control, encryption, masking, retention, and compliance controls.
• Define standards for metadata management, data lineage, data quality rules, issue management, data cataloging, master data, and reference data management.
• Identify data architecture risks and recommend practical mitigation actions to reduce operational, regulatory, privacy, reporting, and security risks.
 
6. Analytics, Reporting, and Business Enablement
• Collaborate with business stakeholders, data analysts, BI developers, and reporting teams to understand information needs and translate them into reliable data structures and consumption layers.
• Support the design of analytical datasets, semantic layers, KPI structures, dashboards, and regulatory reporting data requirements.
• Promote data usability, consistency, trust, and reusability across reporting, analytics, operational insights, and decision-support use cases.
 
7. Architecture Governance and Design Assurance
• Prepare, review, and maintain data architecture documents, data models, architecture diagrams, data flow diagrams, mapping specifications, decision records, and related delivery artifacts.
• Participate in architecture review forums to ensure data solutions follow approved standards, governance controls, integration patterns, and technology direction.
• Provide design assurance during delivery to confirm implemented solutions remain aligned with approved data architecture.
 
8. Technical Leadership, Mentoring, and Continuous Improvement
• Provide technical guidance, coaching, and architectural direction to data engineers, analysts, developers, and implementation partners throughout the data solution lifecycle.
• Promote reusable data standards, best practices, design patterns, naming conventions, and knowledge sharing to improve delivery quality and team capability.
• Stay current with emerging data technologies, cloud services, analytics platforms, governance practices, and industry trends to support innovation and continuous improvement.

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