Data Infrastructure Engineer

Job Description The Data Infrastructure team builds and operates the platform that powers data workloads across PayPay Group. We own the Databricks Lakehouse environment, the cloud infrastructure underneath it, and the tooling that keeps it running — from workspace provisioning and access governance to monitoring, alerting, and incident response. This role sits at the intersection of cloud infrastructure and data platform engineering. You'll design and build AWS infrastructure using Terraform, support Databricks workspace and account operations, and improve platform reliability through SRE practices. Day to day, that means writing IaC, responding to operational issues, shipping platform services, and working directly with data engineers across the group to keep their workloads running smoothly. We're looking for someone who can carry projects independently, thinks about systems beyond the ticket in front of them, and wants to go deeper on data infrastructure as a discipline. You don't need to know Databricks on day one — but you should be comfortable with AWS, Terraform, and the fundamentals of running production infrastructure. Main 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 Qualifications 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 Preferred Qualifications 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