Data DevOps Engineer

Role Overview: At BlackLine, we are seeking a motivated and enthusiastic DataOps Engineer to join our growing data team. As a DataOps Engineer, you will play a crucial role in bridging the gap between data engineering, operations, and development, ensuring the robustness, reliability, and scalability of our data pipelines and infrastructure. If you have a passion for automating data processes, streamlining deployments, and maintaining healthy data ecosystems, we invite you to apply and make your mark at BlackLine. Key Responsibilities: - Develop and Maintain Data Pipelines: Assist in the design, development, and maintenance of scalable and efficient ETL processes to ingest, transform, and load data from various sources into our data warehouse. - Orchestrate Workflows: Implement and manage data workflows using Apache Airflow, ensuring timely execution and monitoring of data jobs. - Containerization and Orchestration: Utilize Docker and Kubernetes to containerize data applications and services, and manage their deployment and scaling in production environments. - Cloud Infrastructure Management & Data warehousing: Work with Google Cloud Platform (GCP) & snowflake services to deploy, manage, and optimize data infrastructure components, including performance tuning and data governance. - Scripting and Automation: Develop and maintain Python scripts for data processing, automation, and operational tasks. - CI/CD Implementation: Contribute to the development and improvement of our CI/CD pipelines for data applications, ensuring efficient and reliable deployments. - System Administration: Provide basic Unix/Linux administration support for data infrastructure, including scripting, monitoring, and troubleshooting. - Monitoring and Alerting: Help implement and maintain monitoring solutions for data pipelines and infrastructure, responding to and resolving incidents. - Collaboration: Work closely with Data Engineers, Data Scientists, and other stakeholders to understand data requirements and deliver reliable data solutions. - Documentation: Contribute to the documentation of data pipelines, processes, and operational procedures. Qualifications Required: - 2-6 years of professional experience in a DataOps, Data Engineering, or a similar role. - Proficiency in Python & SQL for data scripting and automation. - Familiarity with ETL concepts & tools like Apache Airflow, dbt, and experience in building data pipelines. - Hands-on experience with Docker & Kubernetes for containerization. - Experience with Apache Airflow for workflow orchestration. - Working knowledge of at least one major cloud platform, preferably Google Cloud Platform (GCP). - Basic Unix/Linux administration skills. - Familiarity with CI/CD principles and tools. - Strong problem-solving skills and a proactive approach to identifying and resolving issues. - Excellent communication and collaboration skills. - Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience). (Note: Additional details about the company have been omitted as they were not provided in the job description.),