Head of Data Platform Center (Open For expatriate)

·       The Head of Data Platform Center is responsible for leading and managing the data platform team to deliver high-quality, reliable, and scalable data platform & data solutions. 

As the Head of Data Platform Center, you will be responsible for the following:

1.     Leadership and Strategy:

·       Develop and execute the strategic roadmap for the Data Platform Center, ensuring alignment with the organization's overall data and IT strategies.

·       Drive the design and implementation of enterprise data architecture to support group-wide data systems.

·       Provide leadership across all functional areas of the center, including Data Product, Data Engineering, Data Architecture, and CR Management Team.

·       Serve as the key liaison between business units, IT teams, and data stakeholders to ensure alignment and value delivery.

·       Foster a culture of innovation, operational excellence, and continuous improvement in the center.

·       Outcomes/ Measures:

o  The strategic roadmap is successfully developed and implemented, aligning with the overall data and IT strategy.

o  The enterprise data architecture is effectively designed and implemented, supporting group-wide data systems.

o  Effective leadership is provided for all functional areas of the center.

o  Alignment and value delivery are ensured through the key bridging role between business units, IT teams, and data stakeholders.

o  A culture of innovation, operational excellence, and continuous improvement is fostered within the center.

2.     Enterprise Data Architecture:

·       Lead the design and governance of enterprise-wide data architecture principles, frameworks, and concepts.

·       Oversee the alignment of system design and interoperability to ensure consistency across data systems.

·       Collaborate with IT architects to deliver integrated enterprise data and IT strategies.

·       Outcomes/ Measures:

o  The principles, frameworks, and concepts of enterprise data architecture are effectively designed and governed.

o  System design alignment and interoperability are monitored to ensure consistency across data systems.

o  Integrated data and IT strategies are delivered through collaboration with IT architects.

3.     Data Product Management:

·       Manage business requirements analysis and the development of change requests (CRs) for Data Warehouse and related data products.

·       Oversee the design and delivery of data pipelines and reporting systems to support compliance requirements, such as SBV Circular 11.

·       Drive cloud and other data product initiatives, ensuring alignment with the broader Enterprise Data Analytics (EDA) strategy.

·       Outcomes/ Measures:

o  Business requirements are analysed and change requests (CRs) are effectively developed for the Data Warehouse and related data products.

o  Data pipelines and reporting systems are designed and delivered to support compliance requirements, such as SBV Circular 11.

o  Cloud data product initiatives and other data products are driven, ensuring alignment with the broader Enterprise Data Analytics (EDA) strategy.

4.     Data Engineering and Operations:

·       Oversee the design, implementation, and maintenance of end-to-end data pipelines and data processing systems in the enterprise data zone (Data Warehouse).

·       Manage the integration of raw data into downstream systems to serve both business units (for analytics, AVM) and application/system needs.

·       Ensure the stability, scalability, and cost-effectiveness of data provisioning systems and day-to-day operations, including SLA management, data observability, and issue resolution.

·       Lead infrastructure, application support, and security fixes for systems under DPC's management.

·       Outcomes/ Measures:

o  End-to-end data pipelines and data processing systems within the enterprise data area (Data Warehouse) are effectively designed, implemented, and maintained.

o  Raw data is integrated into downstream systems to serve both business units (for analytics, AVM) and application/system needs.

o  Stability, scalability, and cost-efficiency of data delivery systems and daily operations are ensured.

o  Infrastructure support, application support, and security fixes for systems under DPC management are effectively led.

5.     Change Request (CR) Management:

·       Ensure the delivery of CRs meets agreed timelines, business requirements, and quality standards.

·       Lead communication and updates with business functions through appropriate forums to ensure transparency and collaboration.

·       Oversee CR prioritization, resource allocation, SLA management, and performance tracking to optimize service delivery.

·       Recommend and implement process and technology improvements to enhance efficiency in CR delivery.

·       Outcomes/ Measures:

o  CRs are delivered meeting agreed-upon standards for timeliness, business requirements, and quality.

o  Communication and updates with business functions through appropriate forums are effectively led.

o  CR prioritization, resource allocation, SLA management, and performance tracking are monitored to optimize service delivery.

o  Process and technology improvements are proposed and implemented to enhance efficiency in CR delivery.

6.     Team Management and Development:

·       Build and develop a high-performing team across Data Product, Data Engineering, Data Architecture, and CR Management functions.

·       Define clear roles, responsibilities, and performance benchmarks to ensure operational excellence.

·       Mentor and empower team members, fostering a culture of collaboration, innovation, and accountability.

·       Manage resources and cost optimization across the center to ensure sustainable delivery.

·       Outcomes/ Measures:

o  A high-performance team is built and developed across Data Product, Data Engineering, Data Architecture, and CR Management functions.

o  Roles, responsibilities, and performance standards are clearly defined to ensure operational excellence.

o  Team members are guided and empowered, fostering a culture of collaboration, innovation, and accountability.

o  Resources and costs across the center are managed and optimized to ensure sustainable delivery.

v Education

·       Bachelor's or Master’s degree in Computer Science, Engineering, Data Science, or a related field.

v Experience

·       Data Architecture

·       Data Engineering

·       Data Product Management

·       Change Request (CR) Management

·       Cloud Computing & Data Platforms

·       Security & Compliance

·       BI, AI & Machine Learning.

·       Industry Knowledge & Best Practices:

o  In-depth knowledge of data management best practices and industry standards, including data governance, security, and data privacy.

o  Experience in leading the implementation of data-driven strategies that deliver tangible business outcomes.

·       Leadership Experience:

o  10+ years in data-related roles, with at least 5 years in a leadership position managing teams across data, engineering, architecture, and product functions.

o  Experience leading cross-functional teams, preferably in large-scale enterprise data environments.

·       Data Platform Development & Management:

o  Proven track record of overseeing the design, implementation, and maintenance of enterprise-wide data platforms and solutions.

o  Experience in managing cloud-based data infrastructure and ensuring its alignment with business needs.

·       Change Management & Process Improvement:

o  Experience in managing large volumes of change requests, prioritizing and ensuring timely and effective delivery.

o  Track record of implementing process improvements that enhance data pipeline efficiency and reduce operational costs.

·       Working with Business Units & Stakeholders:

o  Experience in collaborating closely with business units to define data product requirements and ensure data systems support business objectives.

o  Familiarity with aligning IT and business goals through data-driven solutions.

·       Technology Expertise:

o  Experience with data technologies (e.g., SQL, NoSQL, Hadoop, Spark) and data platforms (e.g., Snowflake, Redshift, BigQuery).

o  Familiarity with AVML tools and how data platforms can support data science initiatives.

Please contact our senior consultant, Phuong Linh: +84 94 335 6541 (whatsapp) for details