We are looking for an experienced Java Data Engineer to join the Data Streams team within our Trading Development Department.
Trading Development builds and operates the technology behind our trading platform, including real-time trading interfaces, anti-fraud capabilities, support and experience systems, trading data pipelines, and the platforms that connect them all.
As real-time trading data becomes increasingly critical for business decisions, financial metrics, anti-fraud processes, and downstream products, we established the Trading Data Domain to provide deeper specialisation, stronger ownership, and higher-quality data foundations.
The Data Streams team owns the real-time trading data foundation. We transform trading events from MT, EXOT, MMS, and other sources into trusted, enriched, observable, and easy-to-consume data outputs for downstream consumers, services, products, and APIs.
Our goal is to make trading data timely, reliable, well-documented, and correct from source event to final trusted output.
The role is based in our office in Limassol, Cyprus. In case of relocation, we offer full relocation support for you and your family to make your move smooth and worry-free.
What you'll actually do
You will work on systems where data is the core, reliability is critical, and every millisecond matters.
Design and develop scalable streaming data pipelines and integration services using Java, Flink, Kafka, and related technologies.
Build enrichment services that add business, account, symbol, price/tick, and reference context to raw trading events.
Deliver trusted real-time and near-real-time data outputs through Kafka topics, ClickHouse sinks, reusable components, and real-time APIs.
Define and maintain stream contracts, including schemas, field meanings, SLAs, ownership, data guarantees, and consumer expectations.
Ensure data correctness through validation, reconciliation, completeness, consistency, and quality checks.
Build monitoring for freshness, latency, lag, failures, enrichment quality, and consumer impact.
Support production reliability through Kubernetes, GitLab CI/CD, Terraform, resource tuning, deployment processes, and incident analysis.
Maintain existing data solutions while contributing to the transition toward a more reliable and scalable target architecture.
Analyze performance bottlenecks and improve data reliability, processing efficiency, scalability, and quality.
Partner with system analysts, engineers, platform teams, and technical consumers to understand requirements and deliver reliable data services and products.
Who we’re looking for
Strong Java experience in backend, data-intensive, or distributed systems.
Hands-on experience with stream processing technologies such as Apache Flink, Kafka Streams, Kafka Connect, KSQL, or similar. Flink is a strong plus.
Strong knowledge of Apache Kafka, including topics, partitions, consumer groups, delivery guarantees, schema evolution, and production troubleshooting.
Experience designing and operating real-time or near-real-time data pipelines with strong requirements for latency, reliability, correctness, and scalability.
Experience building enrichment, transformation, or event-processing services for high-volume data flows.
Experience building real-time APIs or services on top of trusted data outputs.
Understanding of API design principles, including performance, reliability, versioning, backward compatibility, and consumer contracts.
Experience with data quality, monitoring, alerting, SLAs, production health, and reliability practices.
Good understanding of DataOps, ServiceOps, CI/CD, and DevOps practices.
Experience with Kubernetes, GitLab CI/CD, Terraform, Git, or similar engineering and deployment tooling.
Working knowledge of SQL for validation, troubleshooting, and data quality checks.
Experience with ClickHouse, PostgreSQL, Vertica, BigQuery, or similar data stores. ClickHouse is a plus.
Strong English communication skills for technical discussions, documentation, and collaboration in an international environment.
Strong ownership mindset and accountability for production systems.
Ability to work with ambiguity, investigate problems, and turn them into structured solutions.
Strong problem-solving skills in complex, distributed, real-time systems.
Reliability-first mindset, with focus on data correctness, operational impact, and consumer trust.
Ability to collaborate effectively with engineers, analysts, platform teams, stakeholders, and technical consumers.
Consumer-oriented approach to building clear, stable, well-documented, and easy-to-use data outputs, APIs, and services.
Pragmatic decision-making, balancing architecture, delivery timelines, business priorities, and operational risk.