Site Reliability Engineer

Bidgely; United States; Remote; USD 100,000 - 140,000; Full time

Job summary

A leading company in data processing is seeking a Site Reliability Engineer to enhance system reliability and performance. The candidate will work closely with DevOps and platform teams to develop solutions ensuring Bidgely's services are fast, reliable, and scalable. Responsibilities include incident management, monitoring, and optimizing operational efficiency. A background in Computer Science along with experience in cloud services and automation is essential.

Qualifications

• 3-7 years of experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering.
• Strong automation and scripting skills (Python, Bash, Terraform).
• Proficiency with AWS services and cloud platforms.

Responsibilities

• Ensure high availability and performance of systems and APIs.
• Act as first responder to high-severity incidents.
• Develop strategies to improve data insights accuracy.

Skills

Monitoring

Observability

Automation

Problem-solving

SQL

Education

B. Tech/Bachelor in Computer Science or related field

Tools

Prometheus

Grafana

Datadog

Terraform

AWS

Tableau

Power BI

Job description

As a Site Reliability Engineer (SRE), you will play a pivotal role in ensuring the reliability and availability of Bidgely's data processing infrastructure, API services, and customer-facing applications. You will work closely with our DevOps, Product Support, and Platform & Infra teams to develop and implement solutions that proactively detect, prevent, and resolve operational issues. Your efforts will directly enhance our customers' experience by ensuring that Bidgely's services are fast, reliable, and scalable.

Responsibilities:

• Ensure high availability and performance of critical systems, APIs, and infrastructure components.
• Define and track Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to maintain system reliability standards.
• Develop and maintain error budgets to balance new feature development with reliability.
• Implement and maintain comprehensive monitoring and alerting solutions across critical services, such as APIs, data processing pipelines, databases (e. g., Cassandra, Redshift), and cloud infrastructure.
• Set up proactive monitoring for API latency, system load, throughput, and error rates to identify issues before they impact customers.
• Collaborate with DevOps and Platform, and Infra teams to create end-to-end observability for the entire data processing ecosystem.
• Act as the first responder to high-severity incidents, taking ownership of incident management and response.
• Conduct thorough root cause analysis post-incident, working closely with cross-functional teams to implement long-term resolutions.
• Develop incident runbooks and playbooks to streamline incident response and reduce Mean Time to Recovery (MTTR).
• Implement self-healing solutions for commonly recurring issues, reducing the need for manual intervention.
• Enhance operational efficiency by optimizing resource utilization across infrastructure components like EMR clusters, Redis instances, and SQS queues.
• Perform regular capacity planning to ensure our systems can handle future growth and data processing needs, especially during peak usage periods.
• Collaborate with the Platform and Infra, and DevOps teams to scale infrastructure effectively, ensuring we meet SLAs for data processing and customer response times.
• Monitor and optimize infrastructure costs by ensuring efficient resource allocation and cloud utilization.
• Continuously monitor system performance and optimize APIs, databases, and backend services to reduce latency and improve response times.
• Address performance bottlenecks in the data processing pipeline to ensure timely aggregation, disaggregation, and notification generation.
• Develop strategies to improve the accuracy and quality of data insights provided to customers.
• Documentation and Cross-Team Collaboration:
• Document all reliability processes, runbooks, and incident resolution steps to maintain clear, actionable resources for the team.
• Work closely with Product Support to ensure that customer-impacting issues are resolved quickly, and with DevOps to streamline the deployment and release processes.
• Collaborate on building a culture of reliability and efficiency across the organization.

Key Performance Indicators (KPIs):

• Service Uptime and Availability: Percentage uptime for critical services and systems.
• Mean Time to Recovery (MTTR): Average time to resolve incidents and restore services.
• Incident Frequency: Number of incidents and issues per period, aiming for continuous reduction.
• Error Budget Compliance: Adherence to error budgets without breaching SLOs.
• Automation Coverage: Percentage of manual tasks that have been automated to reduce operational workload.
• Latency and Performance Metrics: API latency (P50 P95 P99) and system throughput for key workflows.

Requirements:

• B. Tech/Bachelor in Computer Science or a related field (math, physics, engineering)
• 3-7 years of experience in Site Reliability Engineering (SRE), DevOps, or Infrastructure Engineering.
• Strong experience with monitoring and observability tools (e. g., Prometheus, grafana, Datadog, ELK stack).
• Proficiency in automation and scripting (e. g., Python, Bash, Terraform) to manage infrastructure as code and automate repetitive tasks.
• Hands-on experience with cloud platforms (AWS preferred) and knowledge of services like EC2 SQS, EKS, S3 RDS, Redshift, EMR.
• Experience with incident management and root cause analysis methodologies.
• Familiarity with database systems (e. g., Cassandra, Redis, MySQL) and large-scale data processing pipelines.
• Excellent problem-solving skills, with a proactive approach to identifying and resolving issues.
• Proficient in SQL (Basic and Advanced) to be able to analyze error and log data.
• and identify patterns to reduce # of recurring issues or identify top opportunity areas to reduce ticket volume.
• Bonus point if he is aware of any reporting tool like Tableau/Power BI /Looker, etc.