Systems & Database Engineer; MariaDB

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.

• Design, deploy, and maintain relational and non-relational database systems at scale (hundreds of millions to billions of records, TB-scale datasets), including MariaDB
  , MongoDB
  , InfluxDB
  , and Click House
  .
• 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.
• Operate confidently in production environments — diagnosing and resolving time-sensitive issues with composure and precision.
• Manage and scale containerized database workloads using Kubernetes, including Click House 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.

• 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.

• 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.