Senior Data Platform Engineer

- CKA/CKAD or equivalent deep Kubernetes expertise - Programming in Go, Rust, or Python - Knowledge of Parquet/Arrow and query engines

- Solid experience designing and operating data pipelines (Kafka, Spark, Airflow, etc.) - Strong Linux fundamentals and scripting (Python and/or Bash) - Deep understanding of database reliability (replication, backups, failover)

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. We operate one of the most demanding data infrastructures in finance, supporting mission-critical distributed systems across multiple database and streaming platforms, with strict requirements around availability and performance. Our environment spans both on-prem infrastructure and AWS, with a strong focus on standardization, automation, and Kubernetes-based orchestration. We are looking for an experienced Senior Data Platform Engineer to join our DevOps team in Paris. This is a senior, hands-on role for an engineer who can challenge and reshape our existing infrastructure, set technical direction, and raise the bar across the team. This role is fully on-site, with a strong focus on ensuring the reliability and scalability of our production data infrastructure.

- Driving and maturing Infrastructure-as-Code practices (Terraform, Helm) - Operating and evolving storage and data platforms across on-prem (VAST) and AWS (S3, EMR, MSK, RDS, EKS) - Establishing standards for Docker images and CI/CD workflows for data services

- Establishing standards for Docker images and CI/CD workflows for data services - Defining and maintaining AWS IAM policies, permissions, and security best practices - Leading complex troubleshooting across the stack (databases, OS, storage, networking)

- Extensive Infrastructure-as-Code experience (Terraform, Helm, etc.), including defining standards and reusable patterns - Strong knowledge of the AWS data ecosystem (S3, EKS, EMR, MSK, RDS) - Deep understanding of AWS IAM and security best practices

- Strong knowledge of the AWS data ecosystem (S3, EKS, EMR, MSK, RDS) - Deep understanding of AWS IAM and security best practices - Hands-on experience with Docker and CI/CD pipelines

- Knowledge of Parquet/Arrow and query engines - AWS certifications - Prior experience in a technical lead or staff-level role

Your responsibilities will include: - Owning the architecture, reliability, and performance of production data systems (ClickHouse, CockroachDB, Trino, etc.) - Evaluating the existing infrastructure, identifying architectural gaps, and driving improvements

- 8+ years in DevOps, Data Platform Engineering, or similar, with experience owning production infrastructure at scale - Deep production experience with distributed data systems (e.g. ClickHouse, Trino, EMR), including performance tuning and architectural decision-making - Expert-level Kubernetes experience (stateful workloads, Helm, operators), ideally at significant scale

- Experience with VAST Data or similar storage systems - Familiarity with Iceberg, Delta Lake or Hudi - Observability tools (Prometheus, Grafana, OpenTelemetry)

- Operating and evolving storage and data platforms across on-prem (VAST) and AWS (S3, EMR, MSK, RDS, EKS) - Establishing standards for Docker images and CI/CD workflows for data services - Defining and maintaining AWS IAM policies, permissions, and security best practices

- Deep understanding of AWS IAM and security best practices - Hands-on experience with Docker and CI/CD pipelines - Solid experience designing and operating data pipelines (Kafka, Spark, Airflow, etc.)

- Familiarity with Iceberg, Delta Lake or Hudi - Observability tools (Prometheus, Grafana, OpenTelemetry) - GitOps tools (ArgoCD, Flux)

- Hands-on experience with Docker and CI/CD pipelines - Solid experience designing and operating data pipelines (Kafka, Spark, Airflow, etc.) - Strong Linux fundamentals and scripting (Python and/or Bash)

- Evaluating the existing infrastructure, identifying architectural gaps, and driving improvements - Designing and operating database clusters on Kubernetes (via operators) at scale - Setting standards and best practices for data pipelines (ingestion, transformation, replication)

- Deep production experience with distributed data systems (e.g. ClickHouse, Trino, EMR), including performance tuning and architectural decision-making - Expert-level Kubernetes experience (stateful workloads, Helm, operators), ideally at significant scale - Extensive Infrastructure-as-Code experience (Terraform, Helm, etc.), including defining standards and reusable patterns

- GitOps tools (ArgoCD, Flux) - CKA/CKAD or equivalent deep Kubernetes expertise - Programming in Go, Rust, or Python

- Setting standards and best practices for data pipelines (ingestion, transformation, replication) - Driving and maturing Infrastructure-as-Code practices (Terraform, Helm) - Operating and evolving storage and data platforms across on-prem (VAST) and AWS (S3, EMR, MSK, RDS, EKS)

- Expert-level Kubernetes experience (stateful workloads, Helm, operators), ideally at significant scale - Extensive Infrastructure-as-Code experience (Terraform, Helm, etc.), including defining standards and reusable patterns - Strong knowledge of the AWS data ecosystem (S3, EKS, EMR, MSK, RDS)