CloudOps Engineer

In our Location 360 team we believe that the location of things, and the relationships between them in time and space, are of fundamental importance to creating transformational digital products. We are passionate about enabling teams to seamlessly incorporate spatial and location data into their applications, analyses, operations, and models. We do this by ingesting and stewarding much of the location related data for Bayer Crop Science, by integrating models that enrich that data, and by building platforms and interfaces that make it easy for teams to integrate that data into their work. Our Environmental team is looking for an experienced and innovative Senior CloudOps Engineer to join us.As a Senior CloudOps Engineer, you will play a crucial role in designing, implementing, and maintaining our cloud infrastructure to ensure the efficient and reliable operation of Bayer's Environmental and Digital Farming product pipelines and services. You will collaborate closely with data engineers, data stewards, data scientists, platform engineers and software developers across the organization to deploy large-scale cloud-based solutions, implement and maintain scalable cloud infrastructures, optimize data pipelines, monitor and identify issues in the cloud environment, enhance system performance and ensure data integrity and security.Key responsibilities include:

Design, deploy, and maintain scalable, secure, and highly available cloud infrastructure on cloud platforms such as Google Cloud Platform (GCP) and AWS.Implement and manage monitoring, logging, and alerting systems to proactively identify and address issues in the cloud environment.Develop and automate deployment processes for efficient infrastructure provisioning and configuration management of cloud resources.Work closely with platform engineers to integrate cloud infrastructure with CI/CD pipelines and deployment workflows.Collaborate with data engineers to optimize data pipelines for performance, reliability, and cost-effectiveness.Conduct regular performance tuning and capability planning to ensure the optimal utilization of cloud resources.Participate in incident response and troubleshooting for production issues in data pipelines and API services.Ensure compliance with industry standards and best practices for data security and regulatory requirements.Stay updated on emerging cloud technologies and best practices and evaluate their potential impact and application in our systems and processes.Provide technical leadership and mentorship to junior team members. Foster a culture of knowledge sharing and continuous learning.Minimum Requirements:

Bachelor's degree in Computer Science, Engineering, or relevant job experience with 7+ years.Significant experience in cloud infrastructure management, preferably in a data-intensive environment.Strong proficiency in cloud platforms such as GCP or AWS, including services like Bigquery, Aurora, GCS/S3, GCE/EC2, Cloud Functions/Lambda, Pub/Sub/SQS/SNS, GKE/EKS, Data Flow, Cloud Spanner, etc.Hands-on experience with Infrastructure as Code (IaC) tools such as Terraform.Proficiency in programming or scripting languages such as GoLang, Python, or Bash for automation and infrastructure management.Experience with containerization technology and orchestration platforms such as Docker and Kubernetes.Experience with monitoring and logging tools such as Grafana, Prometheus, ELK stack, or equivalent.Familiarity with data engineering concepts and tools such as SQL, Kafka, or similar technologies.Solid understanding of networking concepts, security principles, and best practices for cloud environments.Excellent problem-solving skills and the ability to work effectively in a fast-paced, collaborative environment.Strong communication skills and the ability to articulate technical concepts to non-technical stakeholders.Highly proficient in GoLang or Python.Demonstrated experience in cloud cost monitoring and optimization strategies utilizing tools like Google Billing or AWS Cost Explorer to identify cost inefficiencies and implement cost-saving measures.Demonstrated experience in identifying and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs) to ensure system reliability and performance.Experience with multi-cloud environments.Experience with geospatial data processing and analysis tools, or experience working with geospatial datasets.Experience with cloud-based machine learning services and platforms such as Google Cloud VertexAI or AWS SageMaker, and experience with model training, evaluation, and deployment workflows.Basic understanding of data preprocessing techniques such as feature scaling, feature engineering, and dimensionality reduction, and their application in preparing environmental data for machine learning models.

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