What does it mean to "operationalize" an engineering team?

In startup mode, it's the WHY and the WHAT that see the most consideration. The customer problem, the purpose, the service, the deliverable, etc. The HOW is often overlooked, so long as it's "good enough" and delivered as quickly as possible. A common strategy during this phase is "we'll figure it out as we go" – an ad-hoc approach.

As product-market fit is found, companies often choose to focus on growth, expanding their customer base and acquiring as many as possible within their market. This is the "scale-up" mode, and this is where HOW becomes significantly important: how to consistently deliver quality service as customers and usage volume grows exponentially.

It's the intentional tackling of the HOW problem that is the focus of "Operations". This is the work of going from ad-hoc processes and reactive firefighting to a structured, process-driven operation that handles scale efficiently. Here's what operationalizing a reliability engineering team typically involves:

• Playbooks - Creating standard operating procedures for common incidents, outages, and maintenance tasks
• Controls, Automations and self-service - Building tools that enable better control of the systems, or to automate repetitive tasks and allow other teams to help themselves
• Observability and monitoring - Implementation of metrics, logging, and alerting systems that provide visibility into system health and simplify troubleshooting
• Service Level Objectives (SLOs) - Identifying the critical user journeys, establishing clear reliability targets and tracking performance against them
• Refactoring legacy systems - Systematically identifying, updating or replacing early "quick and dirty" solutions with more scalable architectures that can handle increased load and complexity
• Incident management framework - Structured approach to handling incidents with clear roles, communication channels, and post-mortems
• Capacity planning - Regular forecasting of resource needs based on growth projections
• Knowledge management - Documentation systems that capture institutional knowledge and reduce dependency on specific team members
• Cross-training and hiring - Ensuring the team has redundancy in skills and is staffed appropriately for scale
• On-call rotation - Establishing a fair, sustainable schedule for engineers to handle off-hours incidents with clear escalation paths and support systems to prevent burnout

The term "operations" has roots in both military and business process management, where it means to convert strategic goals into day-to-day operational processes. In software development, the language really gained traction with the rise of cloud operations in the 2010s, influenced by Google's approach to running large-scale SRE teams. It boils down to putting something into operation in a systematic, repeatable way.

Operationalizing is building systems, processes, and team structures that can handle a 10x scale without requiring a 10x headcount. While vertical scaling helps with raw capacity, it doesn't address processes that won't scale, or the exponential spikes or system patterns that can lead to cascading failures and erratic behavior.

In addition to efficiencies, a focus on operations is also highly rewarding for both engineers and end-users. In both cases we're investing in predictability, meaning a better and more consistent experience with fewer surprises. For engineers in particular, this can drastically improve work-life balance and increase flow time, reducing burnout.

Of course, it's not as easy as it seems. Especially when it comes to reworking legacy tech while scaling, or identifying and tuning SLOs. These are areas often underestimated, where experienced senior and staff engineers can make a huge difference.

For my next post, I'll tackle the "HOW" of operations – very meta! By that, I mean the heuristics I use to identify priorities and sequencing. I've found mapping activities to be super-useful in surfacing certain insights, such as value-stream mapping, and cognitive load/complexity mapping, and I think it's worthwhile of a post. I might also dip into how qualitative/anecdotal data is another useful driver, like the good work being done by the folks over at getdx.com.