Key skills
TerraformAWS IAMAWS VPCAWS S3AWS EC2AWS multi-account setupsInfrastructure as CodeGitOpsCI/CD pipelinesGitHub ActionsJenkinsPrometheusGrafanaPython scriptingShell scriptingLinux/Unix fundamentalsSecurity best practicesNetworking fundamentalsPythonShellData EngineeringDatabricksSparkKafkaAirflowHadoopAWSKubernetesEKSArgoCDEC2S3IAMGitHubCI/CD
About this role
PayPay is a FinTech company that has rapidly grown to over 70 million users since its launch. They are seeking a Data Infrastructure Engineer to design and manage AWS infrastructure, support the Databricks Lakehouse platform, and ensure reliability through SRE practices.
Responsibilities:
- Design, build, and manage AWS cloud infrastructure using Terraform, following IaC and GitOps practices
- Operate and improve the Databricks Lakehouse platform — workspace provisioning, configuration management, and day-to-day reliability
- Build and maintain platform services and tooling that support data engineers across PayPay Group companies
- Strengthen system reliability through SRE practices: monitoring, alerting, dashboarding, and incident response
- Implement and enforce data governance and security controls in line with compliance requirements
- Collaborate with data engineering teams on infrastructure needs for their projects and workloads
- Participate in on-call rotation and operational support for the data platform
Requirements:
- 3+ years of experience in infrastructure or platform engineering
- Hands-on experience with Terraform and Infrastructure as Code workflows
- Working knowledge of AWS — IAM, VPC, S3, EC2, and multi-account setups
- Familiarity with CI/CD pipelines (GitHub Actions, Jenkins, or equivalent)
- Experience with monitoring and observability tools (Prometheus, Grafana, or similar)
- Scripting ability in Python and/or Shell
- Solid fundamentals in Linux/Unix, networking (DNS, HTTP, basic OSI model), and security best practices
- Experience with Databricks, EMR, or other data platform technologies
- Exposure to Kubernetes and GitOps tooling (EKS, ArgoCD)
- Familiarity with Kafka or event-based data ingestion patterns (CDC, batch consumption)
- Familiarity with workflow orchestration tools (Airflow, Dagster, Prefect)
- Experience with Spark, Hadoop, or large-scale data processing frameworks
- Background in platform engineering — building internal tooling, developer experience, or self-service infrastructure