Data Analyst - Trading Desk

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.

We are looking for a Data Analyst to transform high-velocity data into clear financial insights that inform and strengthen our trading strategies.

Working with real-time data pipelines built on Kafka, RabbitMQ, and ClickHouse, you will help ensure that P&L reporting and trading analytics are accurate, timely, and actionable. Positioned at the intersection of engineering and finance, this role requires strong technical and analytical capabilities, along with a solid understanding of financial markets.

• →Develop and maintain automated PnL reporting frameworks that pull from real-time data streams.
• →Conduct deep-dive trading analytics to identify execution patterns, slippage, and market impact.
• →Write and optimise complex SQL queries in ClickHouse to handle large-scale financial datasets efficiently.
• →Collaborate with stakeholders to define and implement key performance indicators (KPIs) for various trading desks.
• →Monitor data quality and integrity across the pipeline to ensure financial reporting remains compliant and accurate.
• →Use Python to build robust data processing scripts that automate repetitive analytical tasks.
• →Design data models and schemas that specifically support high-frequency reporting and business objectives.

• University degree in Finance, Economics, Computer Science, or a related quantitative field.
• Prior experience working within the finance or fintech industry, with an understanding of financial instruments or trading data.
• Advanced SQL knowledge and experience with OLAP databases like ClickHouse.
• Strong programming skills in Python for data manipulation and automation tasks.
• Experience with data visualisation tools to represent real-time data effectively.
• Ability to convey technical findings clearly to both technical and non-technical stakeholders.
• Detail-oriented mindset with a focus on data accuracy and compliance with governance policies.

• Exposure to ClickHouse (or another columnar/OLAP store — Druid, BigQuery, Snowflake)
• Airflow or similar orchestration; dbt; Git-based workflows
• BI tooling (Superset, Metabase, Power BI, Grafana)