Solution Architecture (Data Platform Solution)

About Us

Do you want to be part of Thailand banking transformation

Data is the core of the new financial services era, and we are open for the opportunity to be part to drive this change at the core.

SCB DATAx is a new venture of the Siam Commercial Bank (SCB) holdings, a leading financial services and digital services holdings in Thailand and ASEAN.

As part of the transformation of SCB into a group of product and technology companies, under the SCBx brand, SCB DATAx is the technology company to centralize data and provides AI and data science services and products to the group.

With a leading-edge cloud native data & AI platform, our vision is to support the group to providing everyone in our region with the opportunity to prosper.

We work on forward-thinking challenges of centralizing, analyzing and sharing information.

We collaborate with companies and experts in many different domains, embrace diversity and all that while having a good laugh and joy in work.

About Team & Role

At DataX, we build the technology platforms that power modern business.

Our products and services focus on cloud infrastructure, data management, and AI-driven applications.

As a member of our Enterprise Architecture team, you'll play a crucial role in connecting our technology strategy to our business vision.

Your mission is to help create and manage secure, effective solutions that drive the company forward.

You'll work alongside a talented group of enterprise, solution, and domain architects specializing in data, applications, and security.

Responsibilities

Solution Architect specializes in Data Platform Architecture, their responsibilities merge the strategic oversight of a solution architect with the deep technical knowledge of data systems.

1. Business Requirement Translation and Strategy

a. Translate Business Goals into Data Strategy

Work with business leaders to understand their objectives (e.g., we want a 360-degree view of our customers or we need to predict inventory shortages) and translate them into a coherent data platform strategy.

b. Define Use Cases

Identify and define the primary use cases for the data platform, such as business intelligence (BI), advanced analytics, and machine learning, and ensure the architecture can support them.

c. Create the Architectural Vision

Develop and champion a long-term vision and roadmap for the organization's data platform, ensuring it can evolve with future business needs.

2. End-to-End Solution Design

a. Architect Data Flow

Design the complete, end-to-end flow of data, from ingestion from source systems to its final consumption by analysts or applications.

b. Design Storage Layers

Architect the central data repository, making critical decisions on the structure and use of a data lake, data warehouse, or a modern lakehouse architecture.

c. Select Technology Stack

Evaluate, select, and justify the appropriate technologies and tools for each component of the platform (e.g., Kafka for streaming, Snowflake or BigQuery for the warehouse, Spark or dbt for transformation).

d. Design for Non-Functional Requirements

Ensure the platform is designed to be:

• i. Scalable: Able to handle increasing volumes of data and users.
• ii. Secure: Incorporating data encryption, access controls, and threat monitoring from the ground up.
• iii. Reliable: Designing for high availability and disaster recovery.
• iv. Performant: Optimized for efficient data processing and fast query responses.

3. Data Governance and Quality Oversight

a. Integrate Data Governance

Design the architecture to enforce data governance policies, including data quality rules, data lineage tracking, and metadata management (data catalogs).

b. Architect for Security and Compliance

Ensure the platform design complies with data privacy regulations like PDPA, GDPR, CCPA, or HIPAA, defining how data is anonymized, masked, and secured.

c. Establish Data Modeling Standards

Set the standards and best practices for how data should be modeled and structured within the warehouse and data marts to ensure consistency and usability.

4. Technical Leadership and Stakeholder Management

Guide Engineering Teams

Provide technical leadership and clear architectural blueprints for data engineers, BI developers, and platform engineers during the implementation phase.

Communicate the Design

Clearly articulate the platform's architecture, benefits, and trade-offs to a wide range of stakeholders, from C-level executives to individual developers.

Manage Technical Risks

Identify and mitigate technical risks and dependencies to ensure the successful delivery of the data platform.

Collaborate with Other Architects

Work closely with security architects, enterprise architects, and application architects to ensure the data platform is well-integrated into the broader IT ecosystem.

Qualifications

1. Education

a. Bachelor's Degree

A degree in Computer Science, Information Systems, Software Engineering, or a related field is typically required.

b. Master's Degree (Preferred)

A postgraduate degree in a data-focused field like Data Engineering, Information Management, or Business Analytics can be beneficial.

2. Professional Experience

a. Core Cloud & Infrastructure

• i. Cloud Platforms: Deep expertise in the data services of at least one major cloud provider e.g. AWS (prefered), Azure, or Google Cloud.
• ii. Infrastructure as Code (IaC): Proficiency with tools like Terraform or AWS CloudFormation to define and manage data infrastructure in a repeatable way.
• iii. Networking & Security: Solid understanding of cloud networking concepts and security best practices for protecting data both in transit and at rest.

b. Data Storage & Warehousing

• i. Modern Data Warehouses: Expert-level knowledge of cloud data warehouses like Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse Analytics.
• ii. Data Lakes & Lakehouses: Deep understanding of data lake design principles on cloud storage (S3, ADLS, GCS) and familiarity with Lakehouse architecture e.g., Databricks Delta Lake (prefered), Apache Iceberg.
• iii. Data Modeling: Strong proficiency in data modeling techniques such as Kimball (star schema), Inmon, or Data Vault.

c. Data Processing & Integration (ETL/ELT)

• i. Orchestration: Hands-on expertise with workflow orchestration tools like Apache Airflow, Mage, or Dagster.
• ii. Large-Scale Processing: Proficiency with distributed data processing frameworks, primarily Apache Spark.
• iii. Data Transformation: Strong skills with modern data transformation tools, especially dbt (data build tool).
• iv. Real-time Data: Experience with data streaming technologies like Apache Kafka, AWS Kinesis, or Google Cloud Pub/Sub.

d. Data Governance & Security

• i. Data Governance Tools: Familiarity with data catalog and governance platforms e.g., Unity catalog (prefered), Collibra, Alation, Apache Atlas.
• ii. Access Control: Experience designing and implementing role-based access control (RBAC) and security policies within the data platform.

3. Soft Skills

• a. Systems Thinking: The ability to see the entire data ecosystem, understand how different parts interact, and design a cohesive, end-to-end solution.
• b. Stakeholder Management: The skill to work with diverse stakeholders from data scientists and analysts to business executives and align them on a unified data strategy.
• c. Communication: The ability to clearly articulate complex data architecture concepts and the business value they provide to both technical and non-technical audiences.
• d. Pragmatism and Trade-off Analysis: Making practical, well-reasoned decisions that balance competing factors like cost, performance, and long-term maintainability.