Data Architect

Position: Data Architect

Duration: 12 Months – Extendable Contract

Location: Cybergate, Mandaluyong City, Metro Manila, Philippines

Work Mode: Hybrid (1WFO & 4WFH)

Work Shift: Day Shift

Job Summary:

The Data Architect plays a critical role in designing and managing the organization's data architecture to ensure availability, integrity, security, and usability of data across the enterprise. This position combines strategic thinking with deep technical expertise in data platforms, governance, and integration to support data-driven decision-making and digital transformation. The Data Architect works closely with business, analytics, and IT teams to develop scalable data models, define data standards, and enable enterprise-wide data initiatives.

Job Qualifications:

Education and Certifications

• Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field (Master's preferred).
• Relevant certifications such as:

1. Certified Data Management Professional (CDMP)
2. AWS Big Data or Data Analytics Specialty
3. Microsoft Azure Data Engineer Associate
4. Google Cloud Professional Data Engineer
5. TOGAF (preferred for enterprise alignment)

Experience

• Minimum of 3 years in data architecture, data engineering, or enterprise data management roles.
• Proven experience designing large-scale data platforms, data lakes, and warehouses (e.g., Snowflake, Redshift, BigQuery).
• Strong background in data modeling, ETL/ELT, data integration, and metadata management.

Skills and Competencies

• Deep understanding of data architecture principles and best practices.
• Proficiency in modern data technologies and languages (e.g., SQL, Python, Spark, Kafka, DBT).
• Strong analytical, problem-solving, and decision-making skills.
• Excellent communication and collaboration abilities to work across technical and business teams.

Job Responsibilities:

Data Architecture Development

• Design and maintain the enterprise data architecture, ensuring alignment with business and analytics strategies.
• Define data modeling standards, metadata management practices, and data lifecycle policies.
• Develop conceptual, logical, and physical data models to support applications, analytics, and reporting systems.

Technology Strategy and Roadmap

• Define and evolve the data architecture roadmap to support data modernization, cloud adoption, and AI/ML initiatives.
• Recommend technology solutions and platforms (e.g., data lakes, warehouses, real-time data processing) based on business needs.
• Stay current with trends in data architecture, data mesh, data fabric, and emerging data technologies.

Collaboration with Stakeholders

• Partner with business units, data analysts, and data scientists to translate business requirements into data solutions.
• Work with application and infrastructure teams to ensure data architecture supports performance, scalability, and resiliency needs.
• Collaborate with the Enterprise Architecture and Cloud teams to ensure end-to-end architectural alignment.

Governance and Compliance

• Define data governance frameworks, standards, and policies in partnership with Data Governance teams.
• Ensure compliance with data privacy regulations (e.g., GDPR, CCPA) and internal security standards.
• Conduct architecture reviews to assess adherence to data quality, lineage, and integrity requirements.

Integration and Interoperability

• Design strategies for seamless integration of data across systems, including cloud, on-premise, and third-party platforms.
• Enable interoperability across business units by defining shared data definitions and interfaces.
• Develop and maintain data APIs and pipelines to ensure reliable data exchange.

Risk Management and Security

• Work closely with cybersecurity teams to implement robust data protection measures.
• Assess and mitigate risks related to data loss, data breaches, and system failures.
• Ensure secure access and role-based permissions for enterprise data assets.

Continual Improvement and Innovation

• Identify opportunities to optimize data flows, storage, and processing using automation and modern tools.
• Foster a data-driven culture by enabling self-service analytics and trusted data assets.
• Support advanced analytics, AI, and machine learning by ensuring a solid data foundation.

Documentation

• Develop and maintain comprehensive data architecture documentation, including models, glossaries, and data dictionaries.
• Contribute to enterprise standards, best practices, and reusable data architecture patterns