Job Description

Roles & Responsibilities

We are looking for a highly motivated Cloud Platform Engineer to join our Cloud Engineering team. The ideal candidate is passionate about data reliability, performance, and scalable backing services. This role focuses on the reliability, performance, operation, automation, and continuous improvement of critical backing services such as MySQL, PostgreSQL, MongoDB, Elasticsearch/OpenSearch, Kafka, analytical databases (e.g., StarRocks, ClickHouse), and other database and messaging technologies across cloud-native and hybrid environments. This is not a traditional DBA role. The ideal candidate understands distributed systems, Kubernetes, cloud platforms, automation, IaC, observability, and AI-assisted workflows, and can help product engineering teams use backing services safely and effectively.

What you'll do

Data Reliability & Backing Services Operations

• Own reliability, performance, scalability, and operational health of MySQL, PostgreSQL, MongoDB, Elasticsearch/OpenSearch, Kafka, StarRocks, ClickHouse, and similar platforms.
• Define best practices for how product engineering teams use transactional, document, search, messaging, and analytical platforms.
• Design and maintain highly available, scalable, and resilient platform services, including replication, backup, recovery, failover, and disaster recovery capabilities.
• Perform capacity planning, performance tuning, workload reviews, upgrades, patching, and lifecycle management for platform services.
• Identify and resolve risks such as slow queries, hot partitions, consumer lag, replication lag, index growth, retention issues, and storage saturation.
• Troubleshoot and resolve complex production issues related to databases, messaging systems, search platforms, and distributed data platforms.

Kubernetes & Cloud Platform Engineering

• Hands-on experience deploying, operating, and troubleshooting stateful workloads in Kubernetes-based environments.
• Strong understanding of Kubernetes fundamentals, including networking, storage, workload lifecycle management, scalability, and reliability concepts.
• Enable and support Kubernetes-based deployments of database, messaging, search, and analytical platforms using cloud-native patterns and operational best practices.

Automation & Platform Enablement

• Use automation, Infrastructure as Code, GitOps, and CI/CD to make backing services repeatable, reliable, and easier to operate.
• Contribute to self-service platform capabilities, guardrails, dashboards, alerts, runbooks, and production readiness checks.
• Use AI-assisted workflows where appropriate for incident triage, root cause analysis, query analysis, capacity forecasting, documentation, and developer support.
• Collaborate with Product Engineering, SRE, Security, Data Engineering, and Cloud Platform teams to improve reliability, performance, availability, and security posture.

Desired Candidate Profile

• 4-7 years of experience in Platform Engineering, SRE, Database Reliability Engineering, Data Platform Engineering, DevOps, or related roles.
• Strong hands-on experience with MySQL, PostgreSQL, Kafka, and at least one of MongoDB or Elasticsearch/OpenSearch in production environments.
• Experience with analytical or distributed data platforms such as StarRocks, ClickHouse, Apache Doris, Druid, Pinot, or similar OLAP systems is highly desirable.
• Hands-on experience operating stateful workloads in Kubernetes-based environments.
• Good understanding of high availability, replication, backup and recovery, disaster recovery, capacity planning, and performance tuning concepts.
• Familiarity with distributed systems concepts including sharding, replication, partitioning, consistency, compaction, backpressure, consumer lag, and query optimization.
• Experience with at least one major cloud platform (AWS, GCP, or OCI).
• Experience automating provisioning, deployment, configuration, monitoring, and lifecycle management using tools such as Terraform, Helm, Ansible, GitOps, or similar automation frameworks.
• Strong scripting or programming skills in Python, Bash, Go, or similar.
• Experience with observability platforms such as LTGM, Prometheus/Grafana, ELK/OpenSearch, Datadog, or equivalent.
• Strong troubleshooting, problem-solving, and debugging skills across distributed systems.
• Excellent communication, collaboration, and documentation skills.
• Demonstrated curiosity, ownership mindset, adaptability, and ability to guide product engineering teams.
• Preferred Qualifications Experience designing, operating, or optimizing large-scale distributed database, messaging, search, or analytics platforms.
• Experience operating or optimizing analytical databases and OLAP systems such as StarRocks, ClickHouse, Apache Doris, Druid, or Pinot.
• Experience with streaming and real-time data platforms leveraging technologies such as Kafka, Flink, Spark, CDC, or similar ecosystems.
• Exposure to Analytics, Data Engineering, AI/ML platforms, LLM-based applications, or AI infrastructure projects.
• Experience using AI-assisted tooling or agents to improve operations, troubleshooting, documentation, or developer self-service.
• Familiarity with modern data architectures and open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi is a strong plus.
• Experience with GitOps practices, CI/CD pipelines, and modern platform engineering methodologies.
• Relevant certifications in Cloud Platforms, Kubernetes, Database Technologies, or Data Engineering are a plus.