September 2024 Summaries

James Sweeney, an Enterprise Sales Account Executive at incident.io, shares his favorite memories, values, benefits, Slack channels, and advice for new joiners in this interview. He emphasizes the company's supportive culture and team mentality, as well as the exciting potential of the incident management space. Sweeney also discusses his day-to-day experience working remotely from a different time zone and prefers a WFH role over a hybrid one.

Herbert Gutierrez, a Technical Support Engineer at incident.io, shares his favorite memory of joining the company and participating in a scavenger hunt around London. He values the "Make it magic" principle that drives the company to provide quick solutions for customers. His favorite benefit is the last Friday of the month, which allows him extra time off with his fiancée. Herbert's favorite Slack channel is #travels, where team members share travel experiences and recommendations. He advises candidates interviewing with incident.io to take a few minutes beforehand to calm their nerves. The company culture can be described as "make it magic," emphasizing seamless and magical solutions for customers. Herbert looks forward to the future growth of incident.io, including going public.

On-call schedules are crucial for businesses operating around the clock to handle urgent situations and ensure business continuity. They distribute operational responsibility evenly, preventing employee burnout and improving team morale. There are various types of on-call schedules, including single person rotations, pairing up, primary and shadow configurations, follow-the-sun, and complex rotations. To create an effective on-call schedule, be clear about the need for it, do your homework on likely impact, determine compensation, decide who's in and out, choose the right configuration, and implement it using a great on-call tool. Best practices for managing on-call shifts include proactively factoring in vacation time, using overrides liberally, and monitoring work-life balance.

Service Level Objectives (SLOs) are specific targets set by a company to measure how well a service should perform. They help maintain system performance, ensure reliability, and minimize downtime. Effective SLOs focus on key metrics like availability, response time, latency, and error rate. Collaboration among internal departments is crucial for setting realistic and achievable SLOs that align with overall business goals. SLOs work together with Service Level Indicators (SLIs) and Service Level Agreements (SLAs) to create a framework for managing service performance and customer expectations. Continuous improvement of SLOs involves iterating, adjusting, error budgeting, and automation where possible.

Incident.io recently welcomed its first cohort of interns, who spent five months working in the company's Engineering team. The interns shared their experiences, highlighting how they were treated as part of the team and trusted to run their own projects. They also discussed their favorite memories, such as off-site trips and shadowing on-call overnight shifts. When asked about their most exciting project, each intern mentioned working on different features that improved the product's functionality and user experience. The interns shared how they have leveled up in various areas during their time at incident.io, including planning and organizing projects, working at a fast pace, and being more confident in sharing their thoughts and ideas. They also praised the company's customer-driven approach and friendly work environment. As they prepare to finish their final year at university, the interns shared valuable lessons they learned during their internship, such as being mindful of time management, not being afraid to ask questions, and focusing on providing value to customers.

Data observability is a crucial aspect of ensuring accurate, consistent, complete, and reliable data for business operations and analytics. It involves validating data against predefined rules and criteria. At incident.io, dbt's native testing features are used to ensure the reliability of their data. They faced two common challenges: relationship tests failing due to timing issues in the ingestion pipeline, and a cumbersome debugging process for failed tests. To address these issues, they implemented customized logic within their data pipeline and utilized dbt's store_failures feature.