Senior/Expert Backend Engineer Trading Platform

About the Company

Our client is a fast-growing technology company in the financial services / FinTech space, building scalable digital platforms and risk infrastructure to support secure, real-time transactions and business operations.

The role is well suited for engineers who enjoy working on distributed systems, real-time data processing, payments, fraud detection, or financial technology platforms.

What You'll Work On

• Build and maintain backend services supporting real-time risk assessment and fraud detection.
• Develop scalable services for executing risk rules, calculations, and automated decision-making.
• Design data pipelines that collect, process, and transform large volumes of transactional and behavioural data.
• Develop internal systems that allow risk teams to configure, test, and manage risk strategies efficiently.
• Improve service performance with a strong focus on availability, throughput, and low latency.
• Work with engineering, product, data, and risk teams to translate business requirements into reliable technical solutions.
• Investigate production issues, improve monitoring, and strengthen overall platform stability.
• Contribute to architecture discussions, engineering standards, and continuous system improvements.

What We're Looking For

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, FinTech, or a related discipline.
• At least 5 year of backend software engineering experience.
• Strong development skills in Go, Python, or another modern backend language.
• Practical experience with common backend technologies such as MySQL, Redis, Kafka, and Elasticsearch.
• Solid understanding of APIs, databases, asynchronous processing, and backend system design.
• Strong analytical and troubleshooting skills.
• Comfortable working with multiple teams and solving problems in a fast-moving environment.

Good to Have

• Previous experience in payments, digital banking, fintech, fraud prevention, or risk management platforms.
• Experience with streaming or big-data technologies such as Flink, Spark, Hadoop, or Kafka-based processing systems.
• Knowledge of real-time decision engines, configurable rule platforms, or fraud detection workflows.
• Experience working with high-volume transactional systems or distributed architectures.
• Exposure to data modelling, statistical methods, or machine learning applications related to risk.
• Experience supporting business or risk teams with technical tools and data-driven decision systems.