Senior Data Platform Engineer

Liven

full-time

Posted on: 9/24/2025

Origin: • 🇮🇩 Indonesia

✨ AI Apply

Senior

AirflowAmazon RedshiftAWSAzureCloudDistributed SystemsDockerFluxGoogle Cloud PlatformGrafanaKafkaKubernetesPrometheusSparkTerraform

About the role

Own and operate the end-to-end data infrastructure, ensuring performance, reliability, and scalability.
Design and implement CI/CD pipelines specifically for data workflows and tooling.
Deploy and manage tools like Airbyte, Prefect, and Superset using Docker and Kubernetes.
Set up and maintain monitoring, secrets management, and alerting systems to ensure platform health and security.
Apply GitOps practices or tools like Argo Workflows for streamlined infrastructure deployments.
Manage and scale Kafka, Spark, or DuckDB clusters to support real-time and batch data workloads.
Explore and maintain self-hosted tools like dbt Cloud, ensuring smooth integration and performance.
Use Infrastructure-as-Code tools like Terraform or Helm to automate provisioning and configuration.
Administer observability stacks such as Grafana and Prometheus for infrastructure visibility.
Implement secure access control, role-based permissions, and ensure compliance with GDPR, HIPAA, and internal data governance standards.
Collaborate across teams to support data engineers, analysts, and developers with reliable infrastructure and workflow tooling.
Steer clear of proprietary infrastructure platforms like AWS Glue or Azure Synapse (we’re staying open-source/cloud-native for now).

5–8 years of experience in DataOps, DevOps, or Platform Engineering roles.
Proficiency with modern data stack components (e.g., Airflow, dbt, Kafka, Databricks, Redshift).
Solid understanding of cloud platforms (AWS or GCP).
Strong communication skills to collaborate across product, data science, and engineering teams.
Bias for ownership, automation, and proactive resolution.
Good to Have: Experience with Infrastructure-as-Code tools like Terraform or Helm for managing Kubernetes and cloud resources.
Good to Have: Familiarity with administering Grafana, Prometheus, or similar observability stacks.
Good to Have: Exposure to GitOps methodologies and tools like Argo CD or Flux.
Good to Have: Hands-on experience with self-hosted or hybrid setups of tools like dbt Cloud.
Good to Have: Understanding of auto-scaling strategies for distributed systems (Kafka, Spark, DuckDB).
Good to Have: Experience contributing to platform or DevOps initiatives in a data-heavy environment.