Senior Data Engineer

 

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

 

Senior Data Engineer 

 

Site Reliability Engineer (SRE)

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ‘Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

 

As an SRE you will bridge software engineering and system administration with a focus on ensuring the reliability, availability, and performance of software systems operating on common cloud infrastructures. You will proactively monitor cloud-enabled pipelines and drive continuous improvement initiatives to improve efficiency and minimize system down time. You’ll work closely with data engineers, cloud engineers, data stewards, software developers, data scientists, and domain experts to build observable and scalable cloud-based infrastructures using modern cloud-native and open-source Infrastructure as Code (IaC) technologies. You’ll push standardization and provide a paved path for other engineers by building and deploying Kubernetes (k8s) Custom Resource Definitions (CRDs) and webhooks.

 

Primary responsibilities:

 

• Monitor and manage observability and alerting of containerized applications and workflows to ensure performance and reliability of assets that meet or exceed stated SLOs;
• Work closely with cloud engineers and platform engineers to build CI/CD pipelines and standardize deployment patterns;
• Provide technical support, incident response, troubleshooting, and resolution for issues;
• Ensure compliance with company and industry standards and best practices for data security and regulatory requirements;
• Stay updated on emerging data engineering technologies and data infrastructures and evaluate their potential impact and application in our systems and processes;
• Collaborate with Staff and Principal Engineers to ensure a cohesive and coherent architecture;
• Communication of technical principles, solutions, and recommendations with business stakeholders, product managers, and other technical leaders;
• Participate in an on-call rotation (but rarely paged/alerted) and be able to occasionally work with flexible hours

 

Required Qualifications:

 

• Cloud & Container Technologies: Strong understanding of cloud platforms (AWS+GCP), Kubernetes, containerization, Linux fundamentals, networking, and related technologies;
• Reliability & Scalability: Strong understanding of distributed systems, system design, error budgets, capacity planning and other patterns that ensure resilient, scalable, and efficient infrastructure;
• Observability & Automation: Strong skills in metrics gathering, monitoring, logging, alerting, automation, and CI/CD to drive efficiency and reduce operational burden;
• Risk & Change Management: Familiarity with risk management, change processes, and SLIs/SLOs;
• Software design: Strong understanding of distributed systems architecture and testing strategies, including load balancing, message queuing and ordering, caching, resiliency and redundancy

 

Preferred Qualifications:

 

• Proficient (3+ years) in Python or Golang with a strong track record of maintaining production data pipelines and backend systems;
• Familiarity with cloud-based machine learning services and platforms such as Google Cloud Vertex AI and experience invoking model endpoints;
• Experience working with customers and developers to deliver full-stack development solution