While Tech Cut Jobs It Quietly Rewired How Engineers Work

Early this year, Meta CEO Mark Zuckerberg declared 2023 the “Year of Efficiency”. The phrase quickly spread across the technology industry. As venture funding tightened and profitability replaced growth as the sector’s priority, technology companies globally including Amazon, Google and Microsoft announced significant layoffs, reshaping engineering teams across the industry.

For the teams that remained, the challenge became maintaining the same pace of delivery with fewer people supporting engineering behind the scenes. Functions such as QA, developer enablement, documentation and Developer Relations came under increased pressure because their impact was often less directly connected to shipping new features.

Yet these teams play a critical role in helping engineers build, test and deliver software efficiently. They reduce friction, improve knowledge sharing and enable developers to focus on solving complex product challenges.

For engineering leaders, this has created a new challenge: how do you maintain speed and quality when the teams responsible for supporting development are no longer growing alongside the products they support? The answer is focused on leverage; finding ways to help existing engineering teams achieve more by improving the systems, processes and tools they rely on every day.

Optimising Software Delivery

One of the areas now facing closer scrutiny is the infrastructure that supports software delivery itself. Testing platforms, CI/CD pipelines, developer tooling and automated testing environments have become essential components of modern tech companies but they also represent a continuously growing cost.

Unlike many engineering investments, the cost of test infrastructure often scales directly with development activity. More commits mean more pipeline runs, more automated tests, increased compute usage and greater storage requirements. Such costs can quickly become significant budget items.

During the rapid growth period of the past few years, many organisations have been willing to accept this trade-off. Speed and expansion have been the priority, with companies investing heavily in developer tooling, cloud infrastructure and automation to help engineering teams deliver faster. However, as the tech sector moves into a new era focused on profitability and efficiency, testing infrastructure has become a natural focus for optimisation.

This trend reflects a broader change happening across the industry. Companies are reassessing their operations, reducing costs and prioritising investments that deliver the greatest impact. While much of the public conversation focuses on layoffs, another transformation is happening inside tech companies: companies are examining the systems, processes and tools that support developers.

The goal should be to increase engineering leverage and help teams deliver the same level of quality and speed with fewer resources. This means identifying areas where automation can remove repetitive work, improving developer workflows and ensuring that engineers spend more time building products rather than maintaining inefficient processes.

As companies look for ways to adapt to this new reality, many are turning to technology and workforce transformation strategies to help teams become more efficient. Beamery, a London-based HR technology unicorn focused on helping organisations develop, retain and move talent internally, is also experiencing these challenges from within.

Dmitrii Kriaklin, a Senior Frontend Engineer at Beamery shares insights into how engineering teams can improve productivity, streamline workflows and support developers in an environment where resources are under increasing pressure.

“When a team becomes smaller, every engineering hour becomes significantly more valuable. In this environment, the teams that succeed are not necessarily those that simply work longer hours, but those that systematically remove unnecessary work and make knowledge more accessible across the organisation. At Beamery, I have been focused on solving these challenges through two key initiatives: optimising integration test execution and automating design system documentation.

One example is a GitHub Actions pipeline I developed that compares compiled artefacts across two branches, identifies the components affected by a change and runs tests only for those areas. This reduced test execution time from around 20 minutes to 3–5 minutes per commit and resulted in estimated annual infrastructure savings of more than £25,000, based on reduced Cypress Dashboard usage and compute costs; a figure expected to grow as the design system scales. For teams operating under tighter constraints, approaches like this make it possible to maintain high product quality without spending unnecessary time and budget on redundant testing.This solution now runs across the shared component library used by 8 product teams and around 30 frontend engineers, so the impact compounds with every commit made against the design system.”

Automating Documentation

Another area where tech companies are looking for efficiency gains is documentation, particularly around design systems, developer resources and internal knowledge sharing.

As companies grow, documentation becomes a critical part of enabling engineering teams to move quickly. Design systems, component libraries and technical guidelines provide a shared foundation for how products are built, helping teams avoid duplicated work, maintain consistency and make decisions faster.

For engineering teams working across multiple products or services, a design system is a source of shared knowledge. It allows developers to understand how interfaces should be implemented, what patterns already exist and how new features can be built without reinventing solutions that have already been created elsewhere.

However, maintaining this information manually becomes increasingly difficult as products and teams scale. Every new component, API change, dependency update or design decision creates additional documentation work. Without a reliable connection between the codebase and documentation, information quickly becomes outdated, creating uncertainty for developers who rely on it.

When enablement and support teams are reduced, or when they do not grow at the same pace as the product, keeping documentation accurate becomes a significant challenge. Engineers then spend more time validating information, asking colleagues for clarification or rebuilding existing solutions. The result is a familiar problem: documentation either becomes outdated or requires dedicated resources that companies no longer have available.

Dmitrii talks about how businesses should approach this challenge:

“In an environment shaped by team reductions and limited expertise, documentation stops being an abstract concern. Every outdated page can translate into additional hours spent rechecking components, resolving inconsistencies and fixing errors. I saw this challenge clearly: multiple product teams were building on top of a shared design system, and any gap between implementation and documentation could lead to interface regressions affecting thousands of users.

In this environment, maintaining documentation manually becomes unsustainable, it either falls behind the codebase or requires a dedicated role that can be difficult to justify when teams are operating with fewer resources.

I addressed this challenge through automation by building a documentation system that reads component types directly from TypeScript code and generates live documentation pages with props and interactive examples. As a result, documentation stopped being a separate artefact that needed constant maintenance and became a by-product of the design system’s codebase itself.

For teams operating under tighter constraints, this approach helps preserve access to product knowledge without requiring a dedicated documentation team.”

Supporting Engineers

While optimising processes and automating repetitive work can significantly improve engineering efficiency, technology alone is only part of the solution. As teams become leaner, companies also need to focus on the engineers responsible for building and maintaining these systems. Another challenge facing tech companies is improving developer productivity and helping existing engineers adapt to a more efficiency-focused way of working.

As businesses shift towards leaner operating models, engineering teams are being asked to take on broader responsibilities without the same level of headcount growth. This makes upskilling a critical part of maintaining productivity, whether through internal knowledge-sharing sessions, mentoring, technical workshops or creating opportunities for engineers to learn from different teams.

Upskilling has become a key part of this process. Engineers are now often expected to understand not only their own codebases, but also areas such as automation, cloud infrastructure, internal platforms and modern development workflows. The challenge for companies is enabling this knowledge transfer without relying on additional support teams or extensive manual guidance.

However, improving engineering capability is not only about technical skills. During periods of change, maintaining strong communication and team connection also becomes essential. Open discussions about challenges, clearer ownership and a culture where engineers can share knowledge openly help teams avoid silos and make better decisions with fewer resources.

Developer experience is therefore becoming a technical priority rather than simply a people initiative. Better tooling, self-service platforms, automated workflows and clearer engineering practices can help teams reduce friction and allow engineers to spend more time solving product problems instead of navigating internal processes.

For tech companies operating with fewer “hands”, improving efficiency is not only about reducing costs but about enabling existing engineers to have a greater impact.