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описание
Senior Big Data & Time-Series Infrastructure Engineer
Engineering | Remote (Europe) | Full-time
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.
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.
задачи
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.
требования
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
Experience in financial markets or trading technology (e.g. FIX protocol)
Background in high-performance hardware/software integration
