Database Engineer

Our Production Services Team is looking for a skilled engineer with a strong foundation in database technologies and systems engineering , not a traditional DBA, but someone who understands how databases fit into broader system architecture and can collaborate across disciplines to drive performance, reliability, and scalability. This role is part of a front office support team, focused on application database engineering for trading platforms and directly supporting traders in a high-performance, low-latency environment. Responsibilities Design, deploy, and maintain relational and non-relational database systems at scale (hundreds of millions to billions of records, TB-scale datasets), including MongoDB, InfluxDB, and ClickHouse. Optimize query performance through advanced indexing strategies, execution plan analysis, and schema design. Develop Python tooling and automation to support database operations, maintenance workflows, and system health. Ensure high availability and reliability through replication, failover design, and disaster recovery planning. perate confidently in production environments — diagnosing and resolving time-sensitive issues with composure and precision. Manage and scale containerized database workloads using Kubernetes, including ClickHouse deployments. Monitor system health using observability tooling and proactively address performance degradation. Collaborate with developers, researchers, and platform engineers to support data-intensive applications. Apply AI tooling and best practices to accelerate engineering workflows. Qualifications Bachelor’s degree in Computer Science, Information Systems, or a related field — or equivalent practical experience. 1-5 years of experience in database engineering, systems engineering, or a related discipline. Strong proficiency in SQL and deep working knowledge of relational databases — complex query manipulation, indexing, partitioning, replication, and high availability. Solid Python development skills — able to write production-quality scripts and tooling, not just one-off automation. Good understanding of Kubernetes — deploying, managing, and debugging containerized workloads. Strong Linux fundamentals — shell scripting, process management, system monitoring, and core command-line fluency. Comfort operating in a production environment — you handle pressure well and take ownership of reliability. Working knowledge of AI tools and best practices for engineering workflows. Nice to Have Graph database experience (e.g., Neo4j or similar). ProxySQL configuration and routing experience. Infrastructure-as-code experience with Terraform. Familiarity with Prometheus and Grafana for observability and alerting. Prior experience in a production or trading environment. #J-18808-Ljbffr System Soft Technologies