SCBX Group is leading the next phase of AI transformation across the SCBX Group. We are building AI capabilities that will redefine how customer experience banking and financial service.This is a strategic, group-level initiative that will shape how millions of customers interact with SCBX products and services.
You will work on large-scale, high-impact problems using modern data and AI technologies, with the opportunity to design and build capabilities that operate at enterprise scale.
As we accelerate this journey, we are expanding a team of curious, hands-on builders who are excited to learn, tackle complex challenges, and turn advanced analytics and AI into real business outcomes.
If you're looking for meaningful work, cutting-edge challenges, and the chance to help shape the future of AI in financial services, we'd love to meet you
In this role, you'll get to
- Design, build, and scale robust data pipeline (batch/streaming) to support large-scale analytics and AI use cases
- Develop and manage data ingestions and transformation workflows from structured, semi-structured, and unstructured sources (e.g. databases, files, APIs, Kafka)
- Build data models and curated datasets to enable downstream analytics, machine leaning, and personalization use cases
- Improve the scalability, reliability, performance, and efficiency of data platforms and processing system
- Develop reusable data frameworks, libraries, and components to accelerate development
- Work closely with Product, Data Analyst, Data Scientists, and AI Engineers to translate business requirements into scalable data solutions
- Ensure data quality, availability, and SLA by collaborating with Data Operations and platform teams
- Implement and maintain CI/CD pipelines for automated data pipeline deployment
- Ensure compliance with data governance, security, and privacy standards
- Advocate for strong data quality, documentation, and engineering best practices across teams
Who we are looking for:
- Passionate about building data foundations that power AI and data products
- Strong in problem solving and able to work with complex, large-scale datasets
- Comfortable working in a fast-paced, cross-functional environment
- Ability design and transform data structures to align with upsteam and downstream system requirements, ensuring seamless indound and outbound data integration
- Proactive, ownership-driven, and focused on delivering reliable solutions
- Curious about modern data technologies and continuously learning
- Able to balance engineering excellence with business impact
- Can do attitude
Technical foundation:
- Strong experience in big data processing and distributed systems
- Proficiency in Python / PySpark, SQL, and Apache Spark
- Hands-on experience building data pipelines and ETL/ELT workflows
- Experience with CI/CD for data pipelines and automated deployment
- Solid understanding of data modeling, data architecture, and data management concepts
- Experience with streaming or real-time data processing (e.g., Kafka or similar)
- Familiarity with cloud-based data platforms (Azure preferred)
- Experience with Azure Databricks or similar big data environments
- Knowledge of data governance, data quality, and security practices
- Experience using Git and software engineering best practices
- Experience with Scala or additional programming languages (preferred)
- Experience with real-time analytics databases or in-memory data stores(preferred)
- Knowledge of Azure cloud services and data ecosystem(preferred)
- Experience working with banking or financial services data(preferred)
- Experience supporting AI / Machine Learning data pipelines(preferred)
Qualifications:
- 5+ years of experience in Data Engineering or related field
- Experience with real-time analytics databases or in-memory data stores
- Knowledge of Azure cloud services and data ecosystem
- Experience working with banking or financial services data
- Experience in supporting AI/ Machine Learning data pipelines
