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TMGM

Senior Data Engineer - Trading Desk

Sydney, New South Wales, Australia

Location
Sydney, New South Wales, Australia
Work
Hybrid
Employment
Full-time
Level
Senior
Function
Data & AI
Pay
Not stated
Posted
12 days ago

About the role

About TMGM

TMGM stands at the forefront of online trading and investment services, proudly serving as Chelsea FC's Official Regional Online Forex and Trading Partner in Asia Pacific. With a global presence and offices spanning three continents, TMGM has earned its reputation as a trusted CFD trading provider. We embody excellence through a commitment to regulatory compliance, technological innovation, and unparalleled customer service.

At TMGM, our vision goes beyond providing a platform for trading; we aspire to empower our clients with the best trading conditions. We leverage industry-leading and cutting-edge innovative platforms to ensure that your trading experience is nothing short of exceptional. Our relentless commitment to research and development continually pushes the boundaries of liquidity and usability, giving you the tools you need to thrive in an ever-evolving financial landscape.

About the role

We are looking for a Senior Data Engineer to play a key technical role in building and enhancing our high-throughput, real-time data ecosystem. You will be responsible for designing scalable and reliable data solutions and supporting the ongoing evolution of our data infrastructure, leveraging technologies such as Kafka, RabbitMQ, Airflow and ClickHouse to power mission-critical financial analytics.

This is a hands-on individual contributor role, rather than a people-management position. You will work closely with a small, collaborative Data team across Sydney and Mainland China, translating complex business requirements into practical, high-performance engineering solutions.

Strong hands-on Python and SQL capabilities are essential, with the ability to independently develop, troubleshoot and optimise data pipelines in a real-time environment.

Responsibility

  • Design, develop, and improve highly scalable, fault-tolerant ETL/ELT pipelines.
  • Build and maintain real-time messaging and stream processing solutions using Kafka and RabbitMQ to ensure sub-second data availability.
  • Work with ClickHouse to optimise complex schema designs, indexing, and query performance for large-scale financial datasets.
  • Deploy and maintain data services in cloud environments, implementing security controls and data governance standards required for the finance industry.
  • Work with business stakeholders and senior leadership to understand business objectives and translate them into practical data solutions. Support fellow data engineers through code reviews and technical guidance.
  • Implement monitoring and automated recovery systems to maintain the integrity and quality of critical financial data.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  • Proven experience in data engineering, with a strong background in designing and implementing ETL processes within cloud environments.
  • Experience within the Finance or Trading technology sector, with a proven track record of handling real-time market or transactional data.
  • Strong programming skills in Python, with experience in developing robust, maintainable, and scalable data processing pipelines.
  • Extensive SQL knowledge and experience.
  • Excellent problem-solving skills and the ability to work collaboratively in a team environment.
  • Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Benefits

  • Competitive salary and performance-based bonuses.
  • Opportunities for career growth and development within a global company.
  • Collaborative and inclusive work environment with exposure to cross-region operations.
  • Health and wellness benefits.
  • Flexible working arrangements.

Source

Posted by TMGM on their own hiring page, apply.workable.com. OptiTalent shows it as published, read 07 Nov 2026 00:26 UTC; the original is the reference.

View the original ad ↗

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