高级大数据及时间序列基础设施工程师

Senior Big Data & Time-Series Infrastructure Engineer
Engineering | Remote (Europe) | Full-time
About the company
Our client is a global fintech software vendor supplying advanced trading technology to leading banks and hedge funds. The company specializes in high-performance, low-latency trading strategy and market data solutions for the financial industry. Engineering is spread across several international development centers, and the working culture is collaborative and fast-moving.
The role
We are looking for a Big Data & Infrastructure Architect to lead the data architecture of a new trading analytics platform. You will be the main authority on the data engine behind real-time and historical analysis of liquidity, order fill quality and market impact. This is a foundational position: you will choose, deploy and tune the core of the analytics product.
Key responsibilities

• Architecture and technology selection: assess and choose the most suitable Big Data / NoSQL engine for high-frequency market data and trade execution logs.

• Infrastructure ownership: take responsibility for installation, configuration, scaling and long-term operation of the database environment.

• Design: define the schema and storage strategy for very large datasets, ensuring high availability and resilience.

• Query and performance tuning: write and optimize complex time-series queries (execution quality and liquidity metrics) so that real-time monitoring tools respond in under a second.

• Knowledge sharing: act as the subject matter expert and coach front-end developers and teammates on efficient ways to query and use the data layer.

• Collaboration: work closely with the team lead and UI developers so the data infrastructure fully supports product needs.

Requirements

• Deep expertise in at least one leading Big Data or time-series database (for example ClickHouse, InfluxDB, or ScyllaDB/Cassandra)

• Proven experience operating high-velocity data environments (streaming ticks, execution logs, order book events)

• Strong skills in writing and tuning complex queries over very large datasets (billions of rows)

• Extensive experience in Linux-centric environments, with a focus on system-level performance and low-latency tuning

• Ability to turn business metrics into efficient data structures, without necessarily writing application-level code

• Excellent English communication skills

Nice to have

• Experience in financial markets or trading technology (e.g. FIX protocol)

• Background in high-performance hardware/software integration

• Automation scripting (Python, Bash)

Originally posted on Himalayas