For years, being a software engineer meant being really good at writing code. If you could architect scalable systems, optimise performance and push clean code, you were set. But today, things are different.

Claude 3.7 Sonnet, GPT-4, and GitHub Copilot

 than ever. AI can now generate boilerplate code, refactor functions, and even suggest architectural improvements.

If your only skill is writing code, you’re at risk of being 

 by those who can do more. The best engineers today aren’t just coding, they’re designing, thinking strategically, and deeply understanding users.

 is more than just a software developer. They don’t just execute someone else’s vision, they help 

 – They don’t just build features; they ensure they are intuitive and easy to use and solve real problems.

 – They care about how things look and feel, not just how they function.

 – Instead of relying on second-hand information from product managers, they engage with customers to understand pain points firsthand.

 – They’re aware of what competitors are doing and where opportunities lie.

 – Instead of fearing AI, they use it to 

. The best engineers today don’t just take orders; they help define the roadmap.

Why UX, Design, and Talking to Users Matter

. Engineers would just build whatever was written in a spec document. But the most successful products today aren’t just feature-rich; they’re 

1. UX is the Difference Between a Good Product and a Great Product

Think about the most popular apps, Apple’s ecosystem, Stripe’s payments dashboard, Figma’s design tools. They all have 

– A product should be snappy and responsive.

 – Users shouldn’t have to think too much to complete a task.

– Small details, animations, and feedback loops improve engagement.

Product Engineers don’t just make things 

2. Good Design Isn’t Just About Aesthetics, It’s About Functionality

Great design isn’t just about how something looks; it’s about 

If a feature is technically impressive but confusing to users, 

If an interface is cluttered, overwhelming, or inconsistent, 

 build products that people actually enjoy using.

Traditional engineers work in silos, they get a ticket and build it. But the best engineers today are 

 to see how they interact with the product.

“Why do people churn? Why do they hesitate before completing an action? What do they wish existed?”

When engineers engage with users directly, they make 

 problems, not just shipping features for the sake of it.

 made sense in an era where software development was slow and expensive. But AI, cloud computing, and modern tooling have 

Now, companies, especially startups, want 

 – Engineers understanding product strategy 

 make smarter trade-offs between speed, performance, and usability.

 – When engineers talk to users directly, they build what 

The best engineers today don’t wait for instructions. They lead.

Empowering engineers to think like product owners isn’t just about tools or individual skills; it’s about changing how teams work and how companies think.

To truly support this evolution, organisations need to foster a culture that values 

, contribute ideas, and take the lead on product decisions.

Breaking down the traditional silos between engineering, design, and product.

Rewarding not just the speed of delivery but the 

This kind of shift doesn’t happen overnight. It requires buy-in from leadership, a mindset of 

great ideas can come from anywhere, especially from those closest to the code and the customer

In an ideal world, this would already be the norm. But increasingly, it's becoming a 

. Especially in a landscape where speed, adaptability, and deep user understanding define the most successful team

talk to our users and potential customers

, ensuring that what we build is both powerful and easy to use.

, making decisions that improve the overall experience.

If you’re an engineer today, ask yourself:

, or are you disconnected from the people using your software?

 changing what makes an engineer valuable. The best engineers of the future won’t be those who can just 

The Rise of the Product Engineer: Why Just Writing Code Won’t Cut It Anymore

You’ve seen developer onboarding go well and also seen it go sideways in ways that make you wonder how companies ever ship anything. Bringing new engineers into a team is like adding a new instrument to a band. If you just hand them a guitar and point to the stage, it will not sound the best. But give them a clear process, some guidance, and a chance to practice, and suddenly, they’re making music.

The problem is, that onboarding isn’t always a priority. Companies focus so much on hiring the best talent, but then they throw them into the deep end with little more than a Slack invite and a Jira ticket. And then they wonder why it takes months for new developers to be productive.

So, let’s talk about how to do this right. Here’s an onboarding checklist that works. One that doesn’t just help new devs survive their first few months but sets them up to thrive.

The best onboarding begins before a developer even logs in for the first time. Think about it: the first days are overwhelming enough without having to chase down access credentials and figure out who to ask for help.


Set up their accounts in advance. Provide a clear onboarding document with links to everything they’ll need, from internal tools to team structures. Give them a buddy or mentor who can answer the “dumb” questions (which, let’s be honest, aren’t dumb at all). Maybe even send them a welcome email with some context about what to expect in their first week. A little preparation goes a long way in making new hires feel like they belong from day one.


2. Make the First Day About People, Not Just Process

Nobody joins a company just to write code. They join a team. Yet, so many onboarding processes focus purely on getting through paperwork and setting up environments.

You should take the time to introduce new devs to their teammates. Schedule one-on-ones with key people they’ll be working with. Have a welcome lunch (virtual or in-person). Consider assigning them a project “tour guide” who can walk them through how things work in the company’s codebase and development workflow. Your goal is to make them feel like part of the team, not just another name in a Slack channel.

Nothing kills enthusiasm faster than spending three days wrestling with local development setup. If you want your new devs to contribute quickly, then remove unnecessary friction.

A good onboarding checklist includes a clear, repeatable way to get up and running:

One-click environment setup (Docker, anyone?)

Clear documentation for installing dependencies

Pre-configured development environments with access to logs, databases, and cloud environments

But we know that environment setup means more than getting code to run locally. Developers must understand how to debug and test their changes effectively.

Your new hires should have immediate access to:

Logging and monitoring tools: Whether it’s Datadog, Grafana or another monitoring stack, they need to know how to track down issues in production.

Debugger setup: Debugging workflows for local, staging and production environments should be clear from day one.

Test environments and CI/CD pipelines: Where do tests run? How do they ensure they don’t break something when pushing changes? A clear introduction to test automation and CI/CD processes removes unnecessary anxiety around deployments.

 as much of this as possible. The faster a dev can push their first PR, the faster they feel like they’re making an impact. Investing in scripts, containerised setups, or developer platforms can drastically cut down on lost time and frustration.

The classic mistake: assigning new developers something massive and vague like “get familiar with the dev environment.” 

Instead, you should guide them through bite-sized, meaningful contributions:

Fix a small bug that introduces them to a key part of the system.

Improve a piece of documentation based on their onboarding experience.

Write a test for an existing feature to understand code quality expectations.

These tasks help them understand the dev environment in a hands-on way while also making them feel useful from the start. They also serve as natural conversation starters for asking questions and learning the team’s workflow.

5. Document the Right Things (and Keep It Updated!)

There’s nothing worse than documentation that’s either nonexistent or so outdated that it’s more confusing than helpful. But let’s be honest. Nobody likes writing docs.

Keep README files current and ensure they actually reflect how things work.

Maintain an internal wiki or Notion page with onboarding guides, team norms, and architectural decisions.

Encourage developers to update documentation as part of their PRs, rather than treating it as an afterthought.

It should also help developers understand who owns what. A new hire should be able to quickly answer:

Where do dependencies live, and how do they interact?

How does deployment work, and what are the post-deployment steps?

Interactive documentation tools (like the features offered by our SkyU platform) can help make ownership clear, providing service maps, API references, and dependency graphs that let developers explore rather than just read static pages.

It's a known fact that new developers often get stuck focusing only on the tasks in front of them. However, understanding the “why” behind the code is just as important as knowing the “how.”

What you need to do is give them a broad view of the product and its users.

Walk them through the customer journey with real user stories.

Show them the company roadmap and how their work fits into larger initiatives.

Explain business goals, product priorities, and technical trade-offs the team is currently navigating.

When your developers understand the impact of their work, they’re more engaged, make better architectural decisions and contribute more meaningfully.

New hires also need to understand how incidents and operational workflows fit into the big picture.

Incident management: What happens when a service goes down? Who gets paged? How do post-mortems work?

Runbooks & playbooks: If a service starts failing, where do they go to diagnose and resolve it?

Security & Compliance: Are there coding practices or security protocols they need to follow from day one?

Understanding the bigger picture helps in making sure developers don’t ship something that accidentally breaks everything else.

Asking questions is a huge part of getting up to speed on things.

The best thing you can do to foster a culture where it’s okay to not know everything. Some ideas:

Have a dedicated Slack channel for onboarding questions where no question is too small.

Make it normal for senior devs to share their learning experiences and past mistakes.

Encourage pair programming, knowledge-sharing sessions, and shadowing opportunities.

A strong support system during onboarding speeds up the learning process and helps new hires integrate into the team more smoothly.

Onboarding doesn’t end after the first week (or even the first month). It takes time to get fully integrated into a team.

A casual end-of-week chat to see how things are going.

A structured 30-day and 90-day review with clear milestones.

Opportunities for new hires to provide feedback on the onboarding process itself.

Onboarding isn’t a one-size-fits-all process, and regular check-ins allow you to adapt based on each developer’s unique experience and needs.

And Here’s Where Things Get Interesting…

You might be thinking: “This all sounds great, but it also sounds like a lot of work.” And you’d be right. Doing onboarding well is work. But it pays off. Teams that get this right see higher retention, faster productivity, and a better developer experience overall.

That’s why some companies are looking beyond just onboarding checklists and toward developer experience platforms that handle the 

. For example, platforms like SkyU are helping teams automate environment setup, streamline workflows, and make it easier for new devs to contribute from day one. When you remove friction, you don’t just onboard developers; you set them up to stay and succeed.

So if you’re still relying on tribal knowledge and outdated Google Docs to get new hires up to speed, maybe it’s time to rethink how you do onboarding. Because a great onboarding experience isn’t just nice to have. It’s a competitive advantage.

Want to see how SkyU can simplify onboarding and improve developer experience? Teams using SkyU reduce onboarding time by 30%.

SkyU’s recent success with a leading RegTech company highlights how automated DevOps workflows can reduce deployment time from a full day to minutes, improving developer onboarding, security, and scalability.

Why Your Dev Onboarding Fails (And How to Fix It)

Building a system takes more than just the development effort; maintaining and covering features takes much more. There is scalability, maintainability, availability, usability. The list goes on and on. With so many things to focus on, how do you keep the platform up and running? A better question might be - what do these even have in common? How do these add value to our systems? Each of these priorities bring value to the table. This includes service reliability alternatively referred to as SR. Yet, the question is how does it add value?

At its core, reliability is about delivering a usable and dependable product for the user. Why focus on the user? Because reliability is all about their experience with your service. If your metrics and measurements don’t account for the user’s perspective, you are missing the point of what reliability truly means.

But why is the customer experience so tied to reliability? Imagine your service has an impressive 99.999% uptime, but customers are dealing with inconsistent performance. In that case, your service is not reliable. On the flip side, if a significant portion of your back-end services are down, but customers don’t notice because of effective mitigations, your service is still reliable.

To dig deeper, we will break reliability into smaller, more manageable pieces, and we will draw on how reliability is defined in manufacturing and engineering, using quality metrics as a guide.

According to the American Society for Quality, reliability is the likelihood that a service will perform its intended function over a specific period. This includes several key components such as probability of success, durability, dependability, availability to perform a function, and quality over time. Let me give you more details about this.

Building Blocks of Service Reliability: What Makes It "Good"!

This is about the ratio of successful requests to unsuccessful ones. For example, in a back-end REST service, a request is considered unsuccessful if it results in server errors, such as HTTP 500 status codes. But what about client errors like 400 (Bad Request) or 401 (Unauthorised)? In a healthy system, these errors should not typically count against the service’s success metrics since they result from incorrect client requests rather than issues with the back-end itself. However, this assumes that the client is functioning correctly and not introducing errors due to defects in its implementation—such as a faulty UI incorrectly invoking the API. Here, we focus on measuring the reliability of the back-end REST service as an isolated component of the system, separate from potential client-side issues.

This means how well your system can recover when things go wrong. If a database fails, can the user pick up where they left off, or is the data corrupted? Does it require a manual cleanup? If an instance of the system goes down, can traffic automatically reroute to another instance? Without a second doubt, fail instances happen in software and durability ensures that your service can handle them gracefully.

A dependable product or service works the way customers expect it to. It delivers the value you’ve promised and meets your service-level agreements (SLAs). While this isn’t always easy to measure directly, customer feedback, reviews, and quality indexes can help. If customers are leaving your product over time, it might be a sign that your service isn’t dependable enough.

Availability, or uptime, is a key part of reliability—but it’s not the whole story. People often confuse the two, thinking they’re the same. However, a service can have high availability but still lack reliability if, for example, it has a low probability of success. Even if only a small percentage of customers are affected by downtime, their experience still matters.

If redundancies or mitigations are in place to minimise the impact of availability issues, reliability can remain strong. For instance, if a service is down in one region or operating at half capacity, but 99% of customer transactions still go through, the overall reliability might still meet expectations.

In software, availability is often measured in “nines.” You might hear terms like “three nines” (99.9%) or “five nines” (99.999%) over a month. Availability is calculated by dividing uptime by the total of uptime and downtime, usually measured over a consistent period like a month or quarter. Keep in mind that not every service needs the same availability target—it depends on its specific use case.

Finally, quality over time helps us understand how reliability and the customer experience evolve. Are things getting better or worse? Are there underlying issues causing these changes? How can we improve or maintain stability? This varies from service to service, but even slow changes in metrics over time can signal areas that need attention.

How Scaling Teams Are Solving the Reliability Puzzle

As teams grow, keeping everything running smoothly gets more complicated. To handle this, companies are moving away from just putting out fires when things go wrong and instead focusing on building reliability into their processes from the start. By using tools like Platform Engineering and Internal Developer Platforms (IDPs), teams can create standardised infrastructure, automate repetitive tasks, and cut down on operational headaches. This makes it easier to deploy updates consistently, even as the team scales.

Advanced observability tools and AI-powered monitoring are also changing the game. They help teams catch potential issues before they turn into major outages, giving them a chance to fix things proactively. Pair that with automated CI/CD pipelines, and you’ve got a system that reduces human error and ensures deployments are stable and repeatable. And let’s not forget Chaos Engineering—it’s like a stress test for your systems, helping you find weak spots and make your architecture more resilient before real users ever notice a problem.

To make sure reliability aligns with business goals, many teams are turning to Service Level Objectives (SLOs). These set clear performance targets so everyone knows what “good” looks like. Add in self-healing infrastructure, which can automatically fix issues and reduce downtime, and you’ve got a recipe for scaling efficiently without sacrificing reliability. By weaving these strategies together, teams can keep systems up and running, boost developer productivity, and deliver a better experience for users—no matter how big they grow.

What strategies have companies embraced to keep SR?

Agile methodologies like Scrum, Kanban, and Lean are essential for maintaining service reliability while scaling teams. By breaking projects into smaller, iterative steps, teams can deliver services incrementally, test outcomes, and adapt quickly to changing demands. This approach ensures that as teams grow, they remain flexible and responsive to customer needs.

For example, cross-functional Agile teams can scale effectively by distributing responsibilities and maintaining clear communication channels. Regular sprint reviews and retrospectives help identify bottlenecks early, ensuring that service reliability doesn’t suffer as the team expands. Agile also fosters a culture of continuous improvement, empowering team members to take ownership and innovate, which is critical for scaling without compromising quality. Agile can also help deliver a product in a way that helps it mature.

Service design ensures that customer-centricity remains at the core of service delivery, even as teams grow. By mapping customer journeys and identifying key touchpoints, teams can design scalable processes that maintain reliability. Tools like customer personas, journey maps, and service blueprints help teams align their efforts, even when new members join or responsibilities shift.

As teams scale, service design provides a clear framework for onboarding new members and ensuring consistency in service delivery. By understanding customer pain points and expectations, teams can prioritise improvements that enhance reliability, regardless of their size. This structured approach prevents service quality from degrading as the organisation grows.

Automation and analytics are game-changers for maintaining service reliability during team scaling. Automation handles repetitive tasks, reduces human error, and frees up team members to focus on higher-value work. This is especially important as teams grow, ensuring that service delivery remains consistent and efficient.

Analytics, on the other hand, provides actionable insights into performance trends, helping teams predict and address issues before they escalate. For example, predictive analytics and AI can analyse historical data to forecast potential service disruptions, enabling teams to proactively mitigate risks. As teams scale, these tools provide the data-driven foundation needed to maintain reliability without overburdening team members.

Quality assurance is another aspect of service reliability, and thus, it can be used to mitigate the risk of reliability concerns. Tools such as Chaos Mesh can be used to test the fault tolerance and recoverability of infrastructure. Further, the availability of components such as API can be measured and approximated using performance testing tools.

Scaling teams successfully requires empowering employees to take ownership of service reliability. This means involving team members in decision-making, providing them with the right tools and training, and encouraging a culture of collaboration and innovation.

When teams feel supported and valued, they are more likely to adapt to scaling challenges and maintain high service standards. For instance, cross-training team members ensure that knowledge is shared, reducing dependencies on specific individuals and making the team more resilient as it grows. Empowered teams are better equipped to handle increased workloads and complexity without compromising reliability.

Stakeholder engagement is critical for maintaining service reliability during team scaling. Keeping customers, employees, suppliers, and partners informed and involved ensures alignment and builds trust. Transparent communication about changes, risks, and benefits helps stakeholders understand how scaling efforts will impact service delivery.

For example, involving stakeholders in feedback loops ensures that their needs are met as teams grow. This collaborative approach minimises misunderstandings and ensures that scaling efforts are aligned with stakeholder expectations. By fostering strong relationships, businesses can scale their teams while maintaining the reliability and quality of their services.

A reliable internal developer platform (IDP) is a game-changer for productivity and trust. Without it, even the most advanced applications can end up causing more headaches than they solve. Service reliability (SR) and IDPs are like two sides of the same coin—they work together to create smooth workflows, encourage team adoption, and speed up innovation.

At SkyU, our in-house IDP is built with reliability as its foundation. We rely on Kubernetes to power our deployment strategy, and its self-healing capabilities are a lifesaver when it comes to minimising downtime. Our deployment pipeline is carefully structured, moving code from development to production with rigorous testing and automated deployments. This not only cuts down on errors but also keeps things stable and predictable.

To keep things secure and running smoothly, we’ve baked in automated checks like vulnerability scanning and static code analysis. SkyU agents keep an eye on critical cluster metrics, sending alerts the moment something seems off so we can tackle issues before they escalate. Health probes are constantly checking to make sure services are up and running, while firewalls and API gateways act as our first line of defence against malicious traffic.

By combining proactive monitoring, automated recovery, and tight security controls, SkyU has built an infrastructure that’s not just robust but also highly reliable. It’s all about giving our teams the confidence to innovate without worrying about the platform letting them down.

Making sure services stay reliable as you scale isn’t just about throwing technology at the problem—it’s about finding the right balance between automation, security, and keeping a close eye on things. When teams start using internal developer platforms, it’s crucial to focus on standardisation, resilience, and observability. These are the building blocks for trust and efficiency.

By investing in things like self-healing infrastructure, well-structured deployment processes, and real-time monitoring, you’re not just making your systems more reliable—you’re also giving developers the freedom to innovate without constantly worrying about things breaking. At the end of the day, reliability isn’t just about keeping the lights on; it’s about creating an environment where teams can consistently deliver value, sustainably and without unnecessary stress.

Beyond the Buzzword: Why Service Reliability is a Must-Have for Scaling Teams

Platform engineering is no longer a niche function within IT. It is the cornerstone of modern digital enterprises. As businesses prioritise scalability, agility, and resilience, platform engineering is at the forefront of enabling innovation through infrastructure optimisation, automation, and AI integration. In 2025, platform teams are evolving from infrastructure providers to strategic enablers, driving business agility and innovation. This article explores key trends shaping the landscape of platform engineering, as informed by Gartner’s Top 10 Strategic Technology Trends for the upcoming year.

In this article, we take you through the hype cycle for platform engineering in 2024 by Gartner. As you can see, platform engineering has experienced a massive surge in recognition, with the discipline now appearing on over 10 different Gartner hype cycles—a nearly 5x increase from last year. Notably, there is now an entire hype cycle dedicated to platform engineering. This growth is mirrored in the platform engineering community, with job titles on LinkedIn and interactions at major conferences like PlatformCon showing a strong, upward trend.

Additionally, infrastructure platform engineering, a key subdiscipline, has been featured in 8 different hype cycles, including those for Cloud Computing, SRE, and I&O Automation. This further highlights the increasing importance of platform engineering in driving innovation and business transformation. As Chief Technology Officers (CTOs) seek to drive their organisations forward, platform engineering emerges as a critical focus area, empowering them to achieve unparalleled levels of efficiency, security, and innovation.

This overview of platform engineering trends will be concise and focused, presenting a streamlined list of key insights.

What is happening to platform engineering in 2025?

By 2026, 80% of large software engineering organisations will establish platform engineering teams as internal providers of reusable services, components, and tools for application delivery — up from 45% in 2022. This reflects the growing recognition of platform engineering's role in simplifying development processes and enhancing productivity, and this is exactly where the AI-powered platform engineering trend first starts to emerge.

As we already know, AI/ML is deeply integrated into platform operations, automating tasks like infrastructure provisioning, resource allocation, and incident response. Thus, AI in platform engineering enhances operational efficiency and reduces manual effort for platform teams. It improves resource utilisation and optimises costs through predictive analytics while enabling faster incident resolution and better application performance with proactive monitoring and anomaly detection.

For example, a platform team can use AI to predict and automatically scale resources based on anticipated demand, ensuring optimal performance and cost-effectiveness.

Platform architectures are becoming more modular and flexible, making it easier for businesses to adapt quickly to changing needs. By reusing pre-built components and services, teams can develop and launch applications faster while staying agile to respond to market shifts and customer demands. This approach also improves scalability and makes platforms easier to maintain over time.

For example, a financial services company could create a platform with reusable modules for authentication, payment processing, and data analytics, allowing it to roll out new financial products more quickly and efficiently.

Case in point, Bob Simms, director of enterprise infrastructure delivery at the US Patent and Trademark Office (USPTO), highlights how their platform engineering teams support over 200 applications by focusing on automation and observability. “We went from weeks to get a deployment done to minutes,” says Simms.

This rapid deployment capability is paired with infrastructure service-level monitoring and automated responses. “If we have a particular type of outage, our observability tool can also restart the application.” Such innovations demonstrate how composable platforms enable both scalability and resilience.

Security is now built into every stage of the software development process, with platform teams playing a key role in enforcing security policies and best practices. This integration helps reduce vulnerabilities and strengthens the overall security posture, while also speeding up the identification and fixing of security issues, by eliminating security-related obstacles, developer productivity increases.

Moving onto an example, a platform team might adopt security-as-code principles, automating security checks and ensuring that security policies are followed throughout the entire application lifecycle.

Further emphasising the role of structured pipelines, Sanjay Srivastava, chief digital strategist at Genpact, explains how platform engineering streamlines processes. “Platform engineering allows us to build a common pipeline and methodology for every product we deploy,” he says. This consistency not only simplifies security enforcement but also addresses talent gaps.

“We found this particularly helpful when it comes to skills and talent, which are hard to find and retain.”

4. Multi-Cloud and Hybrid Cloud Strategies

Platform teams are increasingly managing infrastructure across multiple cloud providers and on-premises environments as part of multi-cloud and hybrid cloud strategies. This approach provides flexibility, scalability, and resilience while optimising costs by leveraging the strengths of different providers. Organisations can mitigate vendor lock-in, enhance disaster recovery, and tailor infrastructure to business needs. For instance, a retail company might adopt a hybrid cloud strategy by using the public cloud for scalable e-commerce and a private cloud for sensitive data, ensuring both performance and security.

For CTOs, these strategies are critical to driving innovation and ensuring business continuity. Multi-cloud adoption reduces dependency on a single vendor, enabling cost-effective scaling and improved risk management. CTOs can leverage these approaches to modernise IT operations, support AI-driven applications, and enhance collaboration across teams, ultimately delivering faster and more reliable services.

Platform engineering plays a key role in enabling these strategies by abstracting infrastructure complexities and automating tasks like provisioning and scaling. Tools such as VMware vRealise, HashiCorp Terraform, and Microsoft Azure Arc help standardise operations, ensuring seamless integration and consistent performance across diverse environments. This allows CTOs to maintain operational efficiency and security while focusing on strategic growth.

A significant majority of enterprises, over 84%, have adopted multi-cloud strategies to enhance cost efficiency, and business agility, and leverage top-tier services from various providers. According to Google, it is expected that more than 85% of organisations will adopt a cloud-first approach by 2025, with over half utilising multi-cloud strategies to foster business innovation and accelerate digital transformation.

In platform engineering, success hinges on providing a great developer experience. Just like a company’s performance is often judged by customer satisfaction, a platform engineer’s impact is measured by how easy and enjoyable it is for developers to work with the platform.

If the platform feels clunky, overly complicated, or inefficient, developers won’t want to use it. And if developers don’t use it, the platform’s value drops significantly.

At PlatformCon2024, Darren Evans (EMEA Practice Solutions Lead, Application Platform) delivered a talk titled "Improving your developers' platform experience by applying Google Frameworks and Methods”, where he discussed Google's HEART Framework, which offers a comprehensive perspective on the developer experience within an organisation by using actionable data. This was a major point in creating developer experience as a trending pinpoint for 2025 in platform engineering.

In platform engineering, the HEART framework can be adapted to fit different goals and situations. For example, enhancing the onboarding experience for new developers might prioritise adoption, task success, and satisfaction. On the other hand, rolling out a new feature could focus more on adoption and happiness. To boost overall platform usage, it may be important to track metrics like engagement, retention, and task success.

Platform teams are increasingly focusing on improving the developer experience by offering self-service tools and easy-to-use interfaces. This shift leads to higher developer productivity and satisfaction, as it reduces the need for platform teams to handle routine tasks. As a result, teams can launch new applications and features more quickly. A platform team might provide a self-service portal that allows developers to provision resources, deploy applications, and monitor their infrastructure without needing constant assistance from the platform team.

Platform engineering in 2025 is evolving rapidly. By embracing these trends, platform teams can empower developers, drive innovation, and deliver exceptional business value. As technology continues to advance, platform teams will play a critical role in shaping the future of digital enterprises.

Platform engineering has become a key focus in IT and operations (I&O) as companies work to handle the growing complexity of cloud-native and distributed systems. Over the past decade, more organisations have adopted platform engineering to simplify workflows and better support their development teams. These efforts have come a long way, proving they can boost scalability and improve developer productivity.

The platform engineering community has also grown, with over 15,000 engineers now sharing ideas and best practices. Events like PlatformCon 2023, which recently held its second edition, show how much interest this field is gaining. These conferences are a great way for CTOs and tech leaders to stay updated on the latest trends, tools, and strategies shaping the future of platform engineering.

This article is based on sources such as Gartner, cio.com, and studies based on industry trends. The specific impact and relevance of these trends may vary depending on the individual organisation and its specific needs.

I hope this article provides a valuable overview of platform engineering trends for 2025!

Top 5 takeaways from the State of Platform Engineering Report 2024

CIOs take note: Platform engineering teams are the future core of IT orgs | CIO

The Role of Platform Engineering in Multi-Cloud Strategies for 2025 | Improwised Technology

How platform engineers can improve their developers’ experience | Google Cloud Blog

Platform Engineering Trends in 2025: A CTO's Guide

This article is aimed at test analysts and other stakeholders who engage in software quality assurance in a managerial capacity. The article discusses some of the unique challenges and opportunities associated with testing an Internal Developer Platform product like SkyU and shares a few reasons why buying one is better than building one in-house.

The International Software Testing Qualifications Board defines testing as “a set of activities to discover defects and evaluate the quality of software artifacts” [1]. Typical software testing activities include activities such as analysing requirements, designing test cases, executing test cases, and comparing expected results with the actual results to identify defects. Based on criteria such as the product architecture, technologies, and business requirements, these testing activities' effort, complexity, and risk tolerance can vary greatly.

To understand this idea, imagine how a product team developing a food order management system for the internal use of an organization might settle for simpler less effortful solutions with higher risks related to the quality of their product compared to a mission-critical food order management system that is built to cater to an entire nation which would demand a much lower tolerance of risk at higher complexity and effort. SkyU, as a PaaS product open to organisations with product development aspirations on both ends of this spectrum, the SkyU QA team has to ensure the product meets the quality assurance expectations of both mission critical software projects as well as software projects catering to trivial business needs.

Challenges and Opportunities Testers of Platform Engineering Products Can Expect

The challenges of testing a platform engineering product like SkyU can be categorised into,

Challenges that arise from the large number of 3rd party services and technologies that need to be bundled in.

Challenges associated with conducting manual and automated end-to-end testing.

Though internal developer platforms are built specifically to meet an organisation's needs, with an OOTB platform engineering product like SkyU, you can't offer a narrow set of features and configurations. This results in the need to support a large number of third party services such as Github Actions, Docker Hub, AWS EKS, and ChatGPT, as well as technologies like Docker, Kubernetes, Helm, and Kustomize. These services and technologies need to work seamlessly across the various layers that make up the product. From a quality assurance perspective, the interconnectedness of these components increases testing activities.

When it comes time to design test cases, a tester will analyse the functionality offered by these 3rd party services and identify how it relates to the functionality offered to the end user. The tester will then employ test design techniques such as 

pairwise testing, decision tables, state transfer diagrams,

 to design the optimal number of test cases to cover the feature. A pain point unique to IDP products to this end is the identification of all the expected results for a given test case.

For example, if a new Cloud CI service is being introduced as a feature (say BitBucket), when creating test cases, a tester may need to identify the expected results to look for in the GUI under the ‘Developer Control Plan’, ‘Integration’, and ‘Delivery Plane’ and the ‘Monitoring Plane sections’. The tester will then need to identify any expected results related to the database that is only accessible through the API layer. Finally, the tester will need to look at the expected results that need to be accessed in the 3rd party service, such as the commit history or pipeline information.

Another pain point associated with testing IDP products is the complexity involved in creating end-to-end test automation scripts. The automation scripts require adaptation to interface across multiple services, architectures, and interface types while maintaining synchronisation between the scripts and the system under test. This is to ensure a reliable test execution.