Senior Cloud Engineer, Observability
Bayer · United States
- Hybrid
- Full-time
- $155,000 / year
- United States
Tailored resume — keyword-matched to this role.
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Job highlights
- Lead observability product development on AWS.
- Standardize service instrumentation and reliability monitoring.
- Reduce MTTR with better detection and triage.
- Integrate observability into CI/CD workflows.
- Mentor teams on operational excellence practices.
About the role
Senior Cloud Engineer, Observability
At Bayer Crop Science’s digital farming arm, we advance regenerative agriculture and technology breakthroughs using agronomic science, data science, engineering, and real-world farming experience. Our shared AWS platform enables hundreds of engineers to ship secure, reliable software faster.
We’re looking for an entrepreneurial builder who treats observability as a product: paved roads for telemetry, opinionated patterns for dashboards/alerts, and a relentless focus on improving signal quality and reducing time-to-detect/time-to-recover. You’ll partner with delivery teams, Security, and Data to standardize how we instrument services, monitor reliability, and learn from production.
Your Tasks And Responsibilities
The primary responsibilities of this role are:
Observability Enablement & Support (Primary Focus)
- Be the hands-on SME for our observability toolchain (e.g., Datadog, CloudWatch, OpenSearch), including log pipelines, tracing/telemetry standards, and platform templates.
- Run office hours, produce exemplars, and pair with teams to implement “known-good” instrumentation and alerting.
- Triage and resolve observability-related platform requests (new service onboarding, log/metric gaps, noisy alerts, dashboard standards) with clear ownership and measurable outcomes.
- Establish and operationalize SLIs/SLOs for key platform components and enable teams to define service SLOs without reinventing the wheel.
Own Observability Paved Roads & Golden Paths
- Maintain opinionated “golden paths” for: Logging (standard fields/tags, retention, searchability), Metrics (naming conventions, cardinality guardrails, standard RED/USE views), Tracing (service maps, critical spans, propagation standards), Dashboards (starter dashboards by service type + curated views for platform reliability).
- Provide reusable templates for alerting patterns (latency, error-rate, saturation, dependency failures), tuned for actionable paging vs. noise.
Reliability Outcomes (Through Signals, Not Heroics)
- Reduce MTTR by improving detection, triage paths, runbooks, and “what changed” visibility.
- Drive reliability reviews focused on observability gaps: missing signals, unclear ownership, bad alerts, and uninstrumented failure modes.
- Partner with delivery teams to turn recurring incidents into durable fixes (instrumentation + alerting + automation + documentation).
Observability + DevSecOps Integration
- Embed observability checks into CI/CD and platform workflows (e.g., telemetry guardrails, dashboard/monitor templates, logging standards checks).
- Partner with Security/Compliance to ensure telemetry supports auditability and incident investigation without ad-hoc effort.
Measure, Learn, Iterate (Ownership Mindset)
- Define and report platform observability KPIs: alert noise rate, % actionable alerts, MTTA/MTTR trends, onboarding time to “fully observable,” runbook coverage, incident recurrence.
- Run lightweight experiments to improve signal quality (threshold tuning, monitor redesign, dashboard UX), and ship improvements like a product owner.
Cost Stewardship for Telemetry (FinOps-Aware Observability)
- Create cost-aware telemetry standards (log volume controls, metric cardinality guidance, sampling strategies, retention tiers).
- Help teams optimize spend while improving reliability outcomes (“cheaper + better” logging/metrics patterns).
Collaboration & Mentorship
- Serve as a trusted partner to delivery units, Security, and Data—turning pain points into paved-road improvements.
- Mentor engineers and uplift organizational practices for incident response, reliability signals, and operational excellence.
Who You Are
Bayer seeks an incumbent who possesses the following:
Required:
- Bachelor’s in computer science/engineering or equivalent experience.
- 5+ years hands-on AWS experience operating production workloads.
- Deep practical experience with observability in production, including: Datadog and/or CloudWatch (dashboards, monitors/alerts, log search, correlation), Designing actionable alerts (noise reduction, ownership, runbook-first alerts), Defining/using SLIs/SLOs and reliability metrics to drive behavior.
- Strong proficiency with Infrastructure as Code (Terraform; CloudFormation a plus).
- Strong programming for automation/tooling (Python, Go, or similar).
- Solid grasp of cloud architecture, networking, and security fundamentals.
Preferred:
- Experience productizing observability enablement (templates, golden paths, standards, onboarding workflows).
- CI/CD at scale (GitLab pipelines), including integrating reliability/telemetry guardrails into delivery workflows.
- Logging/telemetry platforms beyond CloudWatch/Datadog (e.g., ELK/OpenSearch) and experience managing scale concerns (volume, retention, cardinality).
- Container platforms (ECS/EKS) and common AWS data services (RDS/Aurora, S3/lake patterns, MSK/Kinesis).
- FinOps experience related to observability (tagging, allocation, optimizing telemetry cost).
- Relevant AWS certifications and excellent communication skills.
Key skills/competency
- Senior Cloud Engineer
- Observability
- AWS
- Datadog
- CloudWatch
- OpenSearch
- Terraform
- Python
- Go
- Infrastructure as Code
Skills & topics
- Senior Cloud Engineer
- Observability
- AWS
- Datadog
- CloudWatch
- OpenSearch
- Terraform
- Python
- Go
- Infrastructure as Code
- DevOps
- SRE
- Cloud Engineering
- Telemetry
- Monitoring
- Alerting
- CI/CD
- Reliability Engineering
- FinOps
- SLO
- SLI
How to get hired
- Tailor your resume: Highlight AWS, observability tools (Datadog, CloudWatch), and Infrastructure as Code (Terraform) experience. Quantify achievements in reducing MTTR and improving signal quality.
- Showcase automation skills: Emphasize proficiency in Python or Go for developing tooling and automation solutions.
- Demonstrate cloud expertise: Detail your hands-on AWS experience operating production workloads and your understanding of cloud architecture.
- Prepare for technical questions: Be ready to discuss SLIs/SLOs, alerting strategies, and CI/CD integration for observability.
- Research Bayer's culture: Understand their mission of 'Health for all, Hunger for none' and their focus on innovation and collaboration.
Technical preparation
Master Datadog and CloudWatch for dashboards/alerts.,Practice IaC with Terraform for AWS deployments.,Write automation scripts in Python or Go.,Define SLIs/SLOs for key platform components.
Behavioral questions
Describe a time you improved observability.,How do you handle noisy alerts?,Explain your approach to incident reduction.,How do you mentor others on reliability?
Frequently asked questions
- What specific AWS services are most important for a Senior Cloud Engineer, Observability at Bayer?
- For this Senior Cloud Engineer, Observability role at Bayer, deep practical experience with core AWS services like CloudWatch for monitoring and logging is crucial. Proficiency with other relevant AWS services such as OpenSearch (for log search and correlation), ECS/EKS (for container platforms), RDS/Aurora, S3/lake patterns, and MSK/Kinesis (for data services) would also be highly beneficial, as mentioned in the preferred qualifications.
- How does Bayer approach 'productizing observability' for its engineers?
- Bayer views observability as a product by creating 'paved roads' and 'golden paths'. This involves developing standardized patterns for telemetry, dashboards, and alerts, with a strong focus on improving signal quality and reducing detection/recovery times. They aim to provide reusable templates and clear guidance so delivery teams can implement observability effectively without reinventing the wheel.
- What kind of experience is needed to be successful in the Senior Cloud Engineer, Observability role at Bayer?
- Success in this role requires at least 5 years of hands-on AWS experience operating production workloads. You'll need deep practical experience in observability, including tools like Datadog and CloudWatch, designing actionable alerts, and defining SLIs/SLOs. Proficiency in Infrastructure as Code (Terraform) and programming for automation (Python, Go) are also key requirements.
- Can you explain Bayer's 'Observability + DevSecOps Integration' focus for this role?
- This integration means embedding observability checks directly into CI/CD and platform workflows. For example, implementing telemetry guardrails, using standardized dashboard/monitor templates, and ensuring logging standards are met within the delivery process. It also involves partnering with Security and Compliance to ensure telemetry supports auditability and incident investigation efficiently.
- What does Bayer mean by 'Cost Stewardship for Telemetry' in this role?
- Cost stewardship for telemetry involves developing and enforcing cost-aware telemetry standards. This includes guidance on log volume controls, metric cardinality, sampling strategies, and retention tiers. The goal is to help teams optimize their spending on observability data while simultaneously improving reliability outcomes, achieving 'cheaper and better' solutions.
- What are the primary tools for observability mentioned in the Senior Cloud Engineer, Observability job description at Bayer?
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