Role Of Engineers In Product Development

"Our engineers shipped that feature in HOURS, not weeks." That's what a former AWS engineer told me about building their CLI system with almost no product management involvement. While most companies struggle with endless requirements docs and slow feedback loops, his team was responding to customer needs same-day. The difference? They eliminated the barriers between engineers and users. Here are 5 warning signs your product-engineering relationship is fundamentally broken: 🚩 Your engineers have ZERO direct customer contact When developers can't talk to users, they build what they THINK people want, not what they NEED. My most successful teams have engineers who regularly interact with customers. 🚩 Your product team has become a "feature factory" I've seen this countless times - PMs feel they need to justify their existence by constantly shipping new features. This leads to what engineers call "enshittification" - making a product WORSE through over-iteration. 🚩 Engineers wait for spoon-fed requirements If your developers just sit around waiting for detailed PRDs, you're wasting their problem-solving ability. Great engineers have ideas and opinions about what should be built. 🚩 The feedback loop is painfully slow When customer pain → product manager → engineering → solution takes weeks instead of hours, you're killing innovation. The fastest teams cut out these unnecessary layers. 🚩 Engineers have zero creativity or ownership When I hear "the PM didn't prioritize it" as an excuse for not fixing obvious problems, I know the culture is broken. Ownership matters more than process. What actually works? → Engineers talking DIRECTLY to customers → PMs focused on strategy, not micromanagement → Engineers with true ownership → One PM over MULTIPLE teams (forces devs to step up) The secret to those mythical "10x developers" isn't finding unicorns - it's removing the barriers between engineers and users. What other signs have you seen of broken product-engineering relationships?

The most valuable professionals aren't necessarily the deepest specialists - they're the ones who combine multiple skills in ways that create unique value propositions.   Skill stacking involves deliberately developing 2-3 complementary capabilities that rarely exist together in the same person.   This approach creates a competitive advantage that's much harder to replicate than single-skill expertise.   The power comes from positioning yourself at the intersection of different disciplines where you can bridge gaps that others can't:   • Marketing expertise combined with data science creates consumer behavior specialists • Financial analysis paired with technology skills produces fintech innovators • Design thinking merged with strategic planning develops product experience leaders • Sales capabilities enhanced with analytics creates revenue optimization experts   This approach fundamentally changes your competitive landscape.   Instead of competing with thousands of specialists in one area, you're competing with dozens of people who have your specific skill combination.   Companies increasingly value professionals who can translate between different organizational functions and solve complex problems that require interdisciplinary thinking.   The traditional model of deep specialization in one area is becoming less valuable than the ability to connect insights across multiple domains.   For career advancement and pivots, this means focusing on building complementary skills rather than just becoming better at what you already do well.   What skill combinations do you see becoming increasingly valuable in your industry?   Sign up to my newsletter for more corporate insights and truths here: https://lnkd.in/ei_uQjju   #executiverecruiter #eliterecruiter #jobmarket2025 #profoliosai #resume #jobstrategy #skillstacking #careerdevelopment #professionalgrowth #careeradvancement

Show me the money: Skill synergy is as important as the skill itself. New research from the University of Oxford finds that the economic value of, especially Software and Tech skills, is linked to how well they complement with other competencies. For instance, skills like Data Analytics are highly valuable because they seamlessly integrate with other high-value skills, whereas more specialized skills have limited applicability and consequently lower financial value in the labor market. The research highlights the versatility of certain skills that offer high general-purpose value across various sectors. For example, proficiency in the programming language Python (a versatile coding language) can result in an 8% wage increase, emphasizing its broad applicability. In addition, the research shows that skill synergy creates more value in certain sectors. Software & Tech skills are seven times more valuable for workers in Marketing and ten times as valuable for workers in Finance & Legal than for workers from the tech domain. The study’s findings are particularly relevant in the context of a global workforce preparing for the new economy. Three fundamental principles emerge: 1. Skill Sets Most jobs require a combination of skills. Therefore, the value of a skill can only be assessed in the context of its complementary skills. 2. Reskilling Efficiency As workers adapt to new technologies, they incrementally add new skills to their existing skill sets. Maximising complementarity between old and new skills is crucial for economic efficiency in this process. 3. Strategic Value As a particular skill’s set of complementary skills becomes more diverse, the more strategic options a worker has for reskilling. This increases their resilience against unforeseen technological changes in the future. Source: https://lnkd.in/epqUXdAT Press release: https://lnkd.in/emTeMGjF

"Focus on outcomes vs. outputs. Features don't automatically create value" The pivot from an emphasis on outputs to outcomes can be a critical paradigm shift for teams intent on building solutions that provide real value and enhance customer satisfaction. I just wrote about this topic in my new article on LeadDev: https://lnkd.in/gYqwH32S An output signifies the result of an activity (for example, launching a new feature), whereas an outcome is a change in customer behavior that drives business results (e.g. user-happiness improved, thanks to this over time we may see an impact to business metrics like sales). Real-world Scenario Imagine you're Spotify. A newly launched feature (an output), enables listeners to swiftly save their favorite songs (an outcome). The outcome of this results in heightened user satisfaction with usability, an increase in subscription numbers, and a subsequent rise in sales over the following six months (impact). Guiding Teams: Outputs, Impacts, and Outcomes To optimize efficacy, it's imperative to understand the distinctions between guidance based on outputs, impacts, and outcomes: Guidance based on Outputs: This entails asking teams to develop specific new features, products, or enhancements. However, this approach does not inherently ensure the provision of value to the users or the business. Guidance based on Impact: This encourages the team to deliver overarching value, such as revenue enhancement or cost reduction. Although essential, this may not offer explicit direction for the team or assist in the creation of user-oriented solutions. Guidance based on Outcomes: This involves asking teams to create specific customer behavior changes that drive business results. This allows teams to find the right solution and keeps them focused on delivering value to the users and the business. Employing an Outcome-Oriented Approach The value of an outcome-oriented approach is evident when uncertainties abound in a new initiative, product, or feature. Such uncertainties are a common occurrence in software engineering, and outcomes offer a means to define goals that encourage teams to experiment with various solutions until the right one is identified. The Limitations of an Outcome-Oriented Approach In instances where a solution is almost guaranteed to work, such as routine maintenance or bug fixes, the outcome-oriented approach may not be the best fit. For these scenarios, an output-focused plan is more fitting. Occasionally, I'm asked about where elements with no clear link to outcome fit in (like technical debt, code health). This often boils down to perspective (e.g., neglecting these could increase the long-term cost of outcome execution, framing in terms of the value back to the business, but can still have their place). Concentrate on what truly counts. Illustration credit: Workpath: https://lnkd.in/gFRcs-v7 #softwareengineering #motivation #productivity #lifeatgoogle

Amazon's software engineers may soon have to transition to new roles beyond just coding as artificial intelligence continues to advance, as per the insights shared by the company's cloud computing chief. During a candid discussion with Amazon Web Services employees, CEO Matt Garman highlighted the potential transformation in their daily tasks. He mentioned that in approximately 24 months or a similar timeframe, the majority of developers might not primarily focus on coding anymore. This shift indicates a significant evolution in the responsibilities of software engineers. Instead of solely writing code, they might be required to engage in tasks like algorithm design, system architecture, data analysis, or machine learning model training. For instance, developers could focus on optimizing AI algorithms, ensuring data quality for training models, or interpreting machine learning results for business applications. The implications of this potential change are vast. It suggests that the role of software engineers will become more diverse and multidisciplinary, requiring a broader skill set beyond traditional coding abilities. Engineers may need to collaborate more closely with data scientists, domain experts, and business stakeholders to create effective AI solutions that meet specific requirements. This shift aligns with the broader trend of automation and AI disrupting various industries, leading to a reevaluation of skill requirements and job roles. While coding will remain a fundamental skill, the future landscape of software engineering is likely to involve a more strategic and holistic approach to leveraging technology for innovation and problem-solving. Overall, the evolving nature of software engineering within the context of advancing AI technologies presents both challenges and opportunities for professionals in the field. Adapting to these changes and acquiring new skills will be essential for staying relevant and competitive in the rapidly evolving tech industry.

As a client project manager, I consistently found that offshore software development teams from major providers like Infosys, Accenture, IBM, and others delivered software that failed 1/3rd of our UAT tests after the provider's independent dedicated QA teams passed it. And when we got a fix back, it failed at the same rate, meaning some features cycled through Dev/QA/UAT ten times before they worked. I got to know some of the onshore technical leaders from these companies well enough for them to tell me confidentially that we were getting such poor quality because the offshore teams were full of junior developers who didn't know what they were doing and didn't use any modern software engineering practices like Test Driven Development. And their dedicated QA teams couldn't prevent these quality issues because they were full of junior testers who didn't know what they were doing, didn't automate tests and were ordered to test and pass everything quickly to avoid falling behind schedule. So, poor quality development and QA practices were built into the system development process, and independent QA teams didn't fix it. Independent dedicated QA teams are an outdated and costly approach to quality. It's like a car factory that consistently produces defect-ridden vehicles only to disassemble and fix them later. Instead of testing and fixing features at the end, we should build quality into the process from the start. Modern engineering teams do this by working in cross-functional teams. Teams that use test-driven development approaches to define testable requirements and continuously review, test, and integrate their work. This allows them to catch and address issues early, resulting in faster, more efficient, and higher-quality development. In modern engineering teams, QA specialists are quality champions. Their expertise strengthens the team’s ability to build robust systems, ensuring quality is integral to how the product is built from the outset. The old model, where testing is done after development, belongs in the past. Today, quality is everyone’s responsibility—not through role dilution but through shared accountability, collaboration, and modern engineering practices.

I'm seeing startups value a different kind of engineer now. The "10x engineer" is less important than a new flavor we're now helping clients hire for. Meet the Product Engineer. They're a scrappy, get-stuff-done type that combines high technical acumen with high business sense. They don't need a pixel-perfect PRD written out for them in order to start building. A rough idea of the business objective is enough. They check in with stakeholders and even customers along the way to validate why they're building what they're building. They treat the engineering process as an iterative and collaborative journey. They act as their own product manager. Missing details and shifting hypotheses aren't blockers -- they're exciting puzzles to figure out. Above all, they understand the "why" behind what they're doing. The impact they're making is clear to them, and their energy is channeled towards creating momentum. In an early-stage organization, pure technical acumen isn't enough when companies are trying to move fast but also stay lean. A team that's business-minded and in sync about what they're building and why they're building is what outperforms in today's startup environment. We've found that very few engineers hit this early stage product engineer bar -- but they're out there, and enough of them in an organization can be game-changing for your product development velocity.

"At Gong, close to 100% of the features we build end up being used by a significant number of people." To make this happen, every product team at Gong works closely with 6-12 design partners on every new feature and product idea. As a result, unlike what most companies experience, nearly everything they build ends up being widely adopted. In my conversation with Eilon Reshef (co-founder and CPO of Gong), we dig into: 🔸 How specifically Gong's teams work with design partners 🔸 Lessons learned from being early in AI 🔸 Gong's early super-narrow ICP 🔸 Why you should make big decisions quickly 🔸 His “spiral method” for learning complex topics fast 🔸 How and why Eilon encourages radical autonomy Listen now 👇 • YouTube: https://lnkd.in/gQS4Zicp • Spotify: https://lnkd.in/gVra-q6C • Apple: https://lnkd.in/gXezpp79 Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/ 🏆 Think Fast Talk Smart: The Podcast — Tools and techniques to help you communicate more effectively: https://lnkd.in/gfSkiqda 🏆 Vanta — Automate compliance. Simplify security: https://vanta.com/lenny Some key takeaways: 1. At Gong, product teams are organized into autonomous “pods,” each consisting of a product manager, UX designer, a few engineers (front-end and back-end), and fractional roles like analysts and writers. Each pod is assigned a clear job-to-be-done, such as improving sales engagement or forecasting accuracy, and takes full ownership of the product development process. Whether it’s improving sales engagement or forecasting accuracy, each pod runs with full ownership. This is how you get teams to move fast and stay aligned. 2. Gong’s pods don’t just build in isolation—they partner with 12 to 20 design partners (existing customers) who give feedback every step of the way. This constant validation keeps the product on track, with about 95% of features actually getting used. 3. If you want more autonomy and speed, you’ve got to trust your team and give them the freedom to experiment. This means that as a leader, you need to step back, give up some visibility, and accept that you’ll make a few mistakes. The tradeoff is worth it—higher velocity, better morale, and more impactful products. 4. When starting out, extreme focus on a specific customer profile can drive faster success. Gong, for example, initially targeted U.S. companies selling software valued between $1,000 to $100,000 via Webex, narrowing their potential customer base to just 5,000 people. This laser focus enabled quicker product-market fit, word-of-mouth growth, a clear product direction, and easier customer acquisition, demonstrating the power of precision in the early stages.

There’s no real learning or growth in production engineering, it’s too monotonous. A common assumption among many graduating chemical engineers and even those with years of experience is that a production or operations role in a process plant is monotonous. You’re working in shifts, ensuring steady production, monitoring quality, coordinating with maintenance teams, and keeping process equipment running smoothly. The plant layout stays the same, the DCS screens look the same, and the daily activities seem repetitive. But that’s only one side of the story. Even I used to be one of them, thinking that only a Process Design Engineer role truly counts as a ChemE job. However, what most people overlook are the moments that test your skill, presence of mind, and understanding of engineering fundamentals. Troubleshooting emergency situations where time is money, and your one decision can save man, machine, and production loss. It could be (but not limited to) a critical compressor tripping on interlock with no clear cause even after experienced engineers take a look, or a pump cavitating despite ideal suction pressure and temperature conditions. These aren’t routine occurrences, but when they do happen, they demand proactive response and quick decision-making. In those moments, your command of chemical engineering fundamentals and your understanding of plant behavior become the deciding factors. These are the moments when you forget the ASME or API standards for a while and start thinking from first principles. Because in plant operations, every minute of production loss has a cost. Time is not just money, it’s efficiency and reliability. So the next time someone says an operations role is monotonous, remember it’s not about doing the same thing every day. It’s about being ready for the one day when everything goes wrong, and only your expertise can set it right. #ChemicalEngineering #ProductionEngineering #PlantOperations #Troubleshooting #ContinuousLearning

Looks like 100s of engineers joined Cloudflare yesterday and all of them were fired for causing the outage... Twitter was down Canva was down ChatGPT was down Claude was down Even the down detector, the site used to detect the down status of websites, was itself down. But in such a situation, one quality comes in that makes or breaks your caree,r and that is Ownership... Outages don’t ruin careers. Lack of ownership does. When things go down, good engineers don’t panic. They don’t hide. They don’t throw blame. They do three things that decide the next 10 years of their career. 1) They step forward, not backward When a system is burning, most people subconsciously wait for someone else to act. A good engineer says “I’m taking this part, you take the next” and moves fast. Clear roles. Zero ego. You become the person teams trust. 2) They communicate like adults Outages get worse because people stay silent. A good engineer gives short, crisp updates. What we know.  What we don’t. What changed last. Which component is suspicious. Leaders aren’t looking for genius during downtime.  They are looking for clarity. 3) They fix it first, learn later Every outage has two phases: Mitigation and investigation. Good engineers separate them. Bring the system back. Stabilize. Only then open the postmortem and dig. And trust me, when you’ve been around long enough, you realise one thing: Your career doesn’t grow from all the good days. It grows from the three or four bad days where everything collapses at once. Yesterday, the internet broke because of maybe one bad change. It happens. It will happen again. Every company, every stack, every team faces this at some point. But the people who rise after days like this are the ones who say: “Yeah, this was on me. Here’s what happened. Here’s what I’m fixing next.” Ownership is a habit. And nothing will shape your career more than how you behave on your worst technical day.