Senior DevOps DWH Engineer  

Location: Placement in one of our clients in TB Simatupang, Jakarta Selatan

Working System: Full WFO

Employment Contract: PKWT/Contract

Experience Level: Senior Level

- Automation & CI/CD: Design and implement robust CI/CD pipelines (e.g., Jenkins, GitLab CI, Azure DevOps) to automate the deployment of data pipelines (ETL/ELT) and database schema changes.

- Infrastructure as Code (IaC): Develop and maintain scalable, reliable, and secure data infrastructure using Terraform or CloudFormation, particularly focusing on data warehousing services.

- Cloud Platform Management: Manage and optimize data services on cloud providers (AWS, GCP, or Azure), including data warehouses (e.g., Snowflake, Redshift, BigQuery), data lakes (S3, ADLS), and associated compute resources.

- Monitoring & Observability: Implement comprehensive monitoring, logging, and alerting systems (e.g., Prometheus, Grafana, ELK Stack) to ensure the health and performance of the data platform and pipelines.

- Containerization: Utilize Docker and Kubernetes for deploying data-centric applications (like Apache Airflow or Spark clusters) with high availability.

- Security & Compliance: Ensure all data infrastructure and pipelines adhere to organizational security policies and compliance standards.

- Collaboration: Work closely with Data Engineers and Data Scientists to provide self-service tooling and infrastructure that accelerates development velocity.

- Experience: more that 5 years of experience in a DevOps, SRE, or Infrastructure role, with a focus on data platforms.

- IaC Expertise: Expert proficiency with Terraform or CloudFormation.

- Cloud Proficiency: Strong experience managing resources on at least one major cloud platform (AWS, GCP, or Azure).

- CI/CD: Hands-on experience building and maintaining complex CI/CD pipelines.

- Scripting: Strong scripting skills in Python or Bash for automation tasks.

- Data Tools: Familiarity with the operational aspects of modern data warehouses (Snowflake, Redshift, BigQuery) and ETL/Workflow tools (e.g., Apache Airflow).

- Containers: Experience with Docker and basic knowledge of Kubernetes.