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.

About this position

We are seeking a highly skilled Data Engineer with experience in cloud-based data platforms to build scalable, reliable data pipelines and robust data models. This role will work closely with data teams, AI teams, and business stakeholders to ensure a solid data foundation that supports analytics, reporting, machine learning, and downstream data products.

Job Responsibilities

Design, develop, and maintain scalable ETL/ELT data pipelines, including ingestion, cleaning, transformation, and loading into data lakes and data warehouses.
Collaborate with Data Science, BI, Product, and Backend teams to translate business and analytical needs into reliable data models and table structures.
Build and optimize Bronze, Silver, and Gold layers to ensure data consistency, performance, and usability.
Manage batch and streaming data processing frameworks such as Spark, Flink, or Kafka, ensuring system stability and efficiency.
Implement and maintain data quality monitoring, including schema validation, row-count checks, anomaly detection, and pipeline automation.
Provide foundational datasets and feature pipelines to support AI and analytics teams.
Work with platform and infrastructure teams to ensure availability, security, and scalability of the data platform.
Contribute to data governance practices, including metadata management, data cataloging, field definitions, and versioning standards.
Continuously improve pipeline performance, reduce processing costs, and enhance maintainability.

Qualifications

3–5 years of experience in data engineering or backend engineering, with hands-on experience in large-scale data processing.
Bachelor's degree or above in Computer Science, Information Systems, Data Engineering, or related fields.
Strong proficiency in SQL and experience with Python or Scala for data processing.
Experience with at least one major cloud provider (AWS / GCP / Azure); familiarity with S3, Glue, Lambda, Databricks, or similar platforms.
Knowledge of distributed data processing technologies such as Spark, Flink, or Kafka.
Solid understanding of data warehousing concepts and data modeling (Star Schema, Data Vault, Medallion Architecture).
Experience with ETL/ELT pipeline orchestration tools such as Airflow, dbt, or Dagster.
Strong communication skills and ability to collaborate with cross-functional stakeholders.
Detail-oriented, proactive, and strong problem-solving mindset.
Experience in fintech, trading platforms, or risk-related data is a strong advantage.

What we offer

Clear role definition with well-defined objectives
Extensive cross-functional and cross-regional collaboration opportunities
Diverse data scenarios with challenging product strategy initiatives
Fast-paced and dynamic industry environment
Strong sense of ownership
Competitive compensation package within a performance-driven culture

Data Engineer (ETL & Data Warehouse)