Principle Data Architect

About Hytech

Hytech is a leading management consulting firm headquartered in Australia and Singapore, specialising in digital transformation for fintech and financial services organisations. We deliver end-to-end consulting services and provide robust middle- and back-office solutions that enable our clients to optimise operations, enhance efficiency, and stay ahead in a fast-evolving digital landscape. Our client portfolio includes top global trading platforms and leading crypto exchanges.

With more than 2,000 professionals worldwide, Hytech has a strong and growing international presence, with offices across Australia, Singapore, Malaysia, Taiwan, the Philippines, Thailand, Morocco, Cyprus, Dubai, and beyond.

Role Overview

The Data Architect will play a vital role in architecting and implementing scalable, cloud-based solutions using AWS. The successful candidate will be responsible for developing, optimizing, and overseeing our company's conceptual and logical data systems, ensuring that they meet organizational requirements.

Responsibilities

1. Design and implement scalable, high-performance data architectures, focusing on AWS cloud-based solutions.
2. Work closely with stakeholders, software developers, and data analysts to ensure optimal data delivery architecture.
3. Evaluate and recommend new data storage and processing technologies.
4. Migrate on-premises data solutions to AWS cloud services.
5. Utilize AWS services such as Redshift, RDS, S3, Athena, Kinesis, and Glue to implement solutions.
6. Develop data pipelines using related technologies
7. Create and maintain data architecture documentation and best practices.
8. Implement security protocols and disaster recovery processes.
9. Mentor junior staff and perform code reviews.

Requirements

1. Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
2. Strong experience with AWS cloud services, specifically those related to data storage and processing
3. Proven experience in developing data pipelines and analytics code.
4. Knowledge of data modeling tools, diagram tools.
5. Strong understanding of SQL and NoSQL databases.
6. Familiarity with big data platforms like Hadoop, Spark, or Flink is a plus.