Enterprise Data Architect

The Enterprise Data Architect is a senior technical leader responsible for designing, governing, and evolving Maxicare’s enterprise data architecture. This role defines how data is modelled, integrated, stored, governed, and consumed across the organization — ensuring that data assets are trustworthy, accessible, and aligned with both business strategy and regulatory obligations.

The role combines deep data architecture expertise with a strong understanding of enterprise IT

systems and cloud platforms. The Enterprise Data Architect partners closely with data engineers, data analysts, IT architects, business stakeholders, and compliance teams to translate Maxicare’s data ambitions into scalable, governed, and future-ready data solutions.

Experience

Required Qualifications

• Minimum of 8 years of progressive experience in data architecture, data engineering, or
  
  

enterprise IT, with at least 3 years in a dedicated Data Architect or Enterprise Data Architect

role.

• Proven track record of designing enterprise data models and architecture frameworks in large,
  
  

complex organizations — preferably in regulated industries such as healthcare, insurance,

banking, or government.

• Demonstrable experience with data governance programs, MDM initiatives, and metadata
  
  

management platforms.

• Hands-on background with cloud data platforms (AWS Redshift/S3/Glue, Azure Synapse/Data
  
  

Factory, GCP BigQuery/Dataflow, or comparable).

Technical Skills

Data Modelling

• Expert proficiency in conceptual, logical, and physical data modelling.
• Strong command of relational, dimensional (star/snowflake), and NoSQL data modelling
  
  

patterns.

• Proficient with modelling tools such as erwin Data Modeler, IBM InfoSphere, Lucidchart, or
  
  

equivalent

SQL & Programming

• Proficient in SQL including advanced query design, stored procedures, views, and
  
  

performance optimization.

• Working proficiency in Python for data pipeline scripting, data quality automation, and
  
  

architecture prototyping.

Cloud Data Platforms

• Proficient with at least one major cloud data platform: AWS (S3, Redshift, Glue, Lake
  
  

Formation), Azure (Synapse, Data Factory, Purview), or GCP (BigQuery, Dataflow, Dataplex).

• Experience architecting data lake, data warehouse, or lakehouse solutions on cloud
  
  

infrastructure.

Data Integration

• Strong knowledge of integration patterns: ETL/ELT, CDC, API-based, and event streaming
  
  

(Kafka, Kinesis, Event Hubs).

• Familiarity with healthcare data standards: HL7 FHIR, HL7 v2, ICD-10, SNOMED CT.
  
  

Supporting Architecture Knowledge

• Working knowledge of cloud architecture (AWS, Azure, or GCP) including networking, IAM,
  
  

and compute services relevant to data workloads.

• Understanding of microservices architecture and API design as it relates to data contracts and
  
  

service data exposure.

Certifications

Preferred — At Least One Of The Following

• DAMA Certified Data Management Professional (CDMP) — Associate or Practitioner
• AWS Certified Data Engineer – Associate / Professional
• Microsoft Certified: Azure Data Engineer Associate
• Google Professional Data Engineer
• TOGAF 9 or 10 Certified
  
  

Nice to Have

The following are not required but will be considered strong differentiators:

Cloud Security AI / ML Knowledge Healthcare IT

Cloud & Data Security

• Familiarity with data security frameworks applicable to healthcare: CIS Benchmarks, NIST
  
  

CSF, ISO 27001/27701.

• Experience with column-level and row-level security in cloud data warehouses; data masking
  
  

and tokenization for PHI/PII.

• Knowledge of IAM design for data platform access control, including attribute-based access
  
  

control (ABAC) patterns.

• Awareness of Philippine Data Privacy Act compliance at the data layer — data subject rights,
  
  

breach notification, NPC Circular No. 16-01.

Artificial Intelligence & Machine Learning

• Understanding of how data architecture underpins ML/AI pipelines — feature stores, training
  
  

data management, and model serving data flows.

• Familiarity with AI/ML platforms (SageMaker, Azure ML, Vertex AI) and their data architecture
  
  

integration patterns.

• Exposure to Generative AI use cases relevant to healthcare: clinical note extraction, intelligent
  
  

claims triage, member engagement automation.

• Appreciation for AI governance, data bias considerations, and explainability requirements in
  
  

regulated healthcare environments.

Healthcare IT

• Familiarity with Electronic Health Records (EHR) systems and their data integration
  
  

requirements.

• Exposure to PhilHealth interoperability standards and DOH National eHealth Framework data
  
  

requirements.

• Understanding of DICOM for medical imaging data and clinical workflow data flows.
  
  

Core Competencies

• Strategic Thinking — Translates business and clinical goals into long-range data architecture
  
  

strategies.

• Communication — Articulates complex data concepts clearly to both technical and non-
  
  

technical stakeholders.

• Problem Solving — Structured, analytical approach to resolving ambiguous data architecture
  
  

and governance challenges.

• Collaboration — Builds effective working relationships across data engineering, IT,
  
  

compliance, clinical operations, and executive teams.

• Adaptability — Thrives in fast-paced environments where healthcare regulations and data
  
  

technology landscapes evolve continuously.

• Attention to Detail — Produces thorough, accurate data models, architecture documentation,
  
  

and governance artifacts.

• Data Advocacy — Champions data quality, data literacy, and a data-driven culture across the
  
  

organization.

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