Senior Data Platform Engineer

Technologies we use

About the project

Your responsibilities

• Take operational ownership of a self-hosted production data platform.
• Administer and support distributed data technologies such as PostgreSQL, Kafka/Redpanda, Elasticsearch, Druid, Spark or Airflow.
• Monitor data ingestion, streaming, storage, replication, capacity and performance.
• Diagnose production problems across data flows and platform infrastructure.
• Plan and execute upgrades, configuration changes and performance improvements.
• Help establish reliable backup, recovery and disaster-recovery practices.
• Participate in the on-call rotation and respond to production incidents.
• Define and improve monitoring, alerting, dashboards, KPIs and SLOs.
• Investigate complex failures and coordinate their resolution.
• Conduct root-cause analyses and translate findings into preventive improvements.
• Create and maintain runbooks and operational documentation.
• Reduce recurring incidents and manual operational work through automation.
• Support data services running on Kubernetes and private-cloud infrastructure.
• Develop your expertise in operating stateful Kubernetes workloads, including persistent storage, replication, disruption management and safe upgrades.
• Automate infrastructure and platform configuration using tools such as Terraform, Helm, Kustomize or equivalent.
• Improve deployment processes using GitOps and automated delivery practices.
• Contribute to safe, repeatable and observable platform changes.
• Participate in architectural discussions and help shape the future technology stack.
• Challenge existing solutions when a more reliable or scalable approach is available.
• Translate operational and data-platform requirements into supportable technical solutions.
• Work closely with globally distributed technical teams and business stakeholders.
• Use AI-assisted engineering tools pragmatically while maintaining strong quality controls.

Our requirements

• Hands-on experience administering, operating or supporting at least one production data system, such as: PostgreSQL or another substantial database platform
• Kafka, Redpanda or another event-streaming platform;
• Spark or Airflow infrastructure; another distributed or high-volume data technology.
• Understanding of relevant operational concepts such as data ingestion, data flows, partitioning, replication, consumer lag, capacity, performance and recovery.
• Practical exposure to Kubernetes, including deploying, supporting or troubleshooting applications running on it.
• Experience monitoring production systems and resolving technical incidents.
• Experience automating infrastructure or operational work using Infrastructure as Code, configuration management or scripting.
• Ability to write, review or explain scripts and automation code.
• Strong ownership mindset and ability to work independently.
• Clear written and spoken English.

Benefits