Engineering Team Management Skills

As Duarte grew, I’d hear feedback that decisions were made too slowly, which confused me. In reality, we didn’t have a system to recognize when the team was asking for a decision. We thought they were just informing us, so decisions would languish. We weren’t ignoring them, failing to act, or even making incorrect decisions... We just didn’t realize a decision needed to be made in the first place. It dawned on the exec team that the lack of clarity during the conversation is what slows teams down. Leaders and teams can share the same language for decision-making. Much of it is about shaping recommendations that actually lead to the right type of action and making the urgency clear. Here’s the shift that changed everything… We started mapping every decision against two factors: urgency and risk. Low risk, low urgency: Decide without me. Your team runs with it. Low risk, high urgency: Inform on progress. They update you, but keep driving. High risk, low urgency: Propose for approval. They bring a recommendation, and you decide together. High risk, high urgency: Escalate immediately. You're in it together, right now. Once my team understood which quadrant a decision lived in, they knew exactly how to approach me. And I knew exactly what my role was. The framework gave us a shared language. People can’t act on ideas if they don’t understand how decisions are made. Leaders should define how recommendations move from idea to approval to action. That transparency keeps progress from stalling. Remember: One of the biggest threats to your company isn't a lack of good ideas. It's a lack of clarity. #Leadership #ExecutiveLeadership #OrganizationalCulture #DecisionMaking

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.

Most good engineers I worked with had a common trait - they just happen to know a lot of random stuff, facts, and practices at surface level ⚡ The conversations with them were always fun and insightful as they kept telling and sharing interesting nuggets. Although they were not an expert in those, they did have a primitive idea and understanding of what they were talking about. They built this by reading articles and watching videos on seemingly interesting topics that they stumbled upon while surfing the internet. They consumed it even though the topics were unrelated to the domain they worked on. Being open to learning new things is a sign of deep interest in the field and spending time exploring it builds a muscle to learn and grasp varied concepts. An interesting by-product of doing this is cross-pollination, where you can connect the dots draw parallels across fields, and come up with out-of-the-box solutions. The stuff I am talking about may seem like standalone concepts and facts. Some of them are advanced data structures and algorithms, some fragments of database internals, communication protocols, interesting design choices made by some companies, common pitfalls of using a particular tech, etc. Now, these facts and understandings are not learned and built overnight, and neither they are learned at your workplaces. These are built by consistently spending time self-studying. To be honest, this is not difficult to achieve, just make sure you spend some time (say 30 minutes) every weekday to learn stuff that you find interesting and build a genuine interest in those topics. Over time, you will build momentum and turn learning into a habit and find yourself dissecting complex concepts with ease, drawing connections between seemingly unrelated topics, and confidently navigating the landscape. ps: there is no need to sacrifice your weekends, 30 minutes every weekday over 3 years is more than enough time to build a really good understanding and become a better engineer. ⚡ I keep writing and sharing my practical experience and learnings every day, so if you resonate then follow along. I keep it no fluff. youtube.com/c/ArpitBhayani #AsliEngineering #CareerGrowth

Too many engineers walk onto a construction site and simply look. But supervision isn’t about looking — it’s about seeing. They think that showing up in a reflective vest, nodding along, and following instructions is enough. It’s not. On site, your greatest asset isn’t your title — it’s your awareness. Can you see what others miss? Can you understand the technical reality unfolding before you? Supervision isn’t passive. Supervision is an advanced skill. A muscle. A responsibility. If you're supervising engineering works, you’re not there to decorate the site in a reflector jacket. You’re the eyes. The judgment. The first line of quality control. That means: ✅ Knowing what’s right — and why it’s right. ✅ Understanding procedures, not just memorizing them. ✅ Reading specs until they live at your fingertips. ✅ Noticing errors before they become disasters. Supervision is leadership. And leadership demands knowledge. Do you know how rebars should be placed? Can you spot incorrect stirrup angles? Can you tell how many PTR roller passes are needed for compaction? If not, it’s time to learn. f you want to grow fast as a site or project engineer, sharpen your supervision instincts. 1. Know the standard specs — don’t guess, read. 2. Seek to understand the “why” behind site instructions. Ask questions smartly. 3. Observe how experienced engineers give direction, and the reason behind it. 4. Train your eye to notice what others miss. Do not assume. 5. Practice connecting theory with what's happening on-site. And above all — read the damn specs. You won’t master this in one day. But in one year, with intent and discipline, you’ll know more than any classroom could teach you. So start today. Don't just flaunt the reflectors on site. Do not be a passive, invisible GE! Be sharp. Be curious. Be the engineer who sees. That’s the one who leads.

💎 How To Track Your Impact (+ free Notion templates). How to document your small and big wins, visualize your work and the incredible impact you've made ↓ We often assume that good work speaks for itself. If we just work hard enough, our work will get noticed and we will be elevated across our career ladder. Yet more often than not, your achievements will get lost somewhere between reorg efforts, new priorities, abandoned initiatives and urgent deadlines. Managers change all the time. You might have a strong relationship with your manager already, but never get a chance to move up the ladder because they have already moved to another team. A new manager, despite all your efforts, often won’t be able to promote you as an internal policy might block any new promotions in their first 6 or 12 months. So you’ll have to start over again. A good way to push back is to have a “brag document” — a running document that lists your small and big achievements, feedback from your managers and colleagues, screenshots of your appraisals and recommendations, along with lessons you’ve learned. It also builds confidence in your abilities and helps you better see your career trajectory. Useful things to include: 🧠 New skills you’ve learned 🏅 New certificates you’ve acquired ⏱️ Impactful projects you’ve leaunched 🧪 Experiments or A/B tests you’ve initiated 🧭 Product metrics you’ve moved 👋 Onboarding sessions you helped with 🚀 Changes you’ve initiated 🗣️ Workshops you’ve conducted 🧑🏫 Mentoring sessions you’ve coached 🌟 Endorsements you’ve received 🤝 Collaboration wins across departments 🧹 How you’ve dealt with design debt 📦 Successful scoping and getting buy-in 🛠️ Tools or systems you’ve introduced 🔧 Bugs or issues you proactively resolved 📣 Coordinating communication in teams 🔮 Lessons you’ve learned 🧯 Conflicts you’ve resolved There are plenty of things that can go in such a document. Typically it’s a simple Notion page or a Google Doc that you set up once and keep updating regularly. One useful habit that can help there is to always update the document after a retrospective session with your team and around a month later. The reason for that is that you’ll need to accumulate and add concrete evidence and results of the impact of your work. Typically business metrics are lagging metrics, so it will take a while until you get some results. One word of caution: it doesn’t work well if you update in huge and bulky batches as memories become a bit blurry and details get lost. Also, don’t think just about the design work — work also happens outside of the design work as we saw in the list above. Also, as Stephen Kernan noted once, whenever possible, try linking your accomplishments to the career ladder one level above your current role. If you can prove that you’ve been performing at the next level for past 3-6 months, you will make the case for your promotion strong and more obvious. (Useful templates in the comments below ↓)

How do you continue to be technical as you grow as a leader? That is a mentoring question I got from a mid-management professional in a session last evening. She felt that as a people manager she wasn't getting opportunities to be very 'technical'. First and foremost, I strongly believe you cannot (and should not) lead technical teams without continuing to be technical - devolving into a project manager is the fastest way to lose trust with your teams. The key though is to define what is 'technical' and how that evolves as you grow in your roles. Here are some thoughts on how you will continue to be 'technical' beyond writing code. #1 Identify how much you value each hour of your time and what is the best value you can drive with your team with that time. So, while coding on a Jupyter notebook may not be the best way to spend my time, doing technical reviews to understand and guide teams on the approach, trade-offs and outcomes still is technical. For example - One of my teams worked extensively on NLP to understand customer issues and we oft did technical deepdives on BERT vs Word2Vec, what data do we use for training, trade-off decisions and success criteria. Or aligning on the experiments your teams run and how success and roll outs look like. #2 The key role you play as a leader in technical teams is to define where your team invests energy and time. A lot of this is in defining the roadmap & the success criteria - the portfolio of projects that will drive the OKRs, alignment with business stakeholders, and technical support required (Inhouse or third-party resources etc). Unblocking your teams to do their job is a key part you play as a leader. #3 Resource optimization and alignment - An understated space that impacts success is the structure of teams. Aligning on how PODs should work, how they work together, aligning folks to their skillsets, grooming your next set of leaders on the team etc is key #4 Advocacy, Storytelling and Influencing without authority - A lot of the work you tend to do also happen to be 'connecting the dots'. Translating outputs of the models to outcomes, illustrating the 'so what', advocating and communicating value back to stakeholders, customers and telling the story with data is a critical skill. #5 Continuous Learning - If you are leading technical teams, the learning never stops. You probably are not the expert on the team in terms of Autogen but understanding the fundamentals of the products you use, the upcoming tech trends, product releases, implications and pre-mortems allow you to guide your teams better. So apart from people management, being technical means learning where you intervene as a leader - a skill worth its weight in gold. What else would you add? *********************************************** Ranjani Mani #reviewswithranjani #Technology | #Books | #BeingBetter

I was Wrong about Influence. Early in my career, I believed influence in a decision-making meeting was the direct outcome of a strong artifact presented and the ensuing discussion. However, with more leadership experience, I have come to realize that while these are important, there is something far more important at play. Influence, for a given decision, largely happens outside of and before decision-making meetings. Here's my 3 step approach you can follow to maximize your influence: (#3 is often missed yet most important) 1. Obsess over Knowing your Audience Why: Understanding your audience in-depth allows you to tailor your communication, approach and positioning. How: ↳ Research their backgrounds, how they think, what their goals are etc. ↳ Attend other meetings where they are present to learn about their priorities, how they think and what questions they ask. Take note of the topics that energize them or cause concern. ↳ Engage with others who frequently interact with them to gain additional insights. Ask about their preferences, hot buttons, and any subtle cues that could be useful in understanding their perspective. 2. Tailor your Communication Why: This ensures that your message is not just heard but also understood and valued. How: ↳ Seek inspiration from existing artifacts and pickup queues on terminologies, context and background on the give topic. ↳ Reflect on their goals and priorities, and integrate these elements into your communication. For instance, if they prioritize efficiency, highlight how your proposal enhances productivity. ↳Ask yourself "So what?" or "Why should they care" as a litmus test for relatability of your proposal. 3. Pre-socialize for support Why: It allows you to refine your approach, address potential objections, and build a coalition of support (ahead of and during the meeting). How: ↳ Schedule informal discussions or small group meetings with key stakeholders or their team members to discuss your idea(s). A casual coffee or a brief virtual call can be effective. Lead with curiosity vs. an intent to respond. ↳ Ask targeted questions to gather feedback and gauge reactions to your ideas. Examples: What are your initial thoughts on this draft proposal? What challenges do you foresee with this approach? How does this align with our current priorities? ↳ Acknowledge, incorporate and highlight the insights from these pre-meetings into the main meeting, treating them as an integral part of the decision-making process. What would you add? PS: BONUS - Following these steps also expands your understanding of the business and your internal network - both of which make you more effective. --- Follow me, tap the (🔔) Omar Halabieh for daily Leadership and Career posts.

That VP who barely knows your work just vetoed your promotion. "Not enough strategic presence," they said. After coaching Fortune 100 leaders, here's what I've discovered: ➟ Strong team results ➟ Outstanding metrics ➟ Top performance reviews Yet when promotion time arrives, someone in the leadership room says: "I'm not sure they're ready." What's really happening? The Executive Trust Gap. Take Sarah, a Senior Engineering Manager who led a $14M product launch. Despite stellar metrics (98% team retention, 42% faster delivery), her CPO said: "Great execution, but I need to see more strategic leadership." Three months later, using what I'm about to share, she got promoted and now leads high impact meetings which opens doors to career-defining opportunities. The truth? Trust influences promotion decisions more than performance metrics alone. Here are 7 strategic moves that turn skeptical executives into your biggest champions: 1. Master the executive language shift ↳ Junior leaders talk about activities ("I completed the project") ↳ Senior leaders talk about outcomes ("This delivered 20% growth") ↳ Top leaders talk about strategic implications ("This positions us to...") ↳ Frame your updates at the highest appropriate level 2. Volunteer for cross-functional initiatives ↳ Creates visibility with multiple decision-makers ↳ Shows your impact beyond your immediate role ↳ Proves you think about the broader business 3. The "Preview" Strategy ↳ Brief key stakeholders before big meetings ↳ "I want to share our approach first and get your input" ↳ Eliminates surprise (which executives hate) 4. Create "Trust Deposits" before needing withdrawals ↳ Share relevant industry insights without asking for anything ↳ Congratulate executives on company wins ↳ Build the relationship when stakes are low 5. The 10-minute rule for executive meetings ↳ Practice delivering your message in 10 minutes ↳ Then practice delivering it in 5 minutes ↳ Then practice delivering it in 2 minutes ↳ Be ready for any time constraint 6. Demonstrate intellectual honesty ↳ Address problems before they're mentioned ↳ Acknowledge limitations in your recommendations ↳ Shows judgment and builds confidence in your thinking 7. The "Proxy Champion" technique ↳ Identify who already has the executive's trust ↳ Build strong relationships with these proxies ↳ Their endorsement becomes your shortcut to trust The most qualified person rarely gets the promotion. The most trusted one does. Which of these 7 moves will you implement this week? ♻ Repost to help someone bridge their trust gap. ➕ Follow me for more proven leadership strategies that create real career momentum.

If you entered tech in the last 5-7 years, you grew up learning the fundamentals the hard way. You debugged without Copilot. You read docs that hadn't been summarized by ChatGPT. You struggled through concepts until they stuck. That struggle built something AI can't replace: judgment. Now layer AI tooling on top of that foundation, and you've got an engineer who can ship at speeds that would've taken a full team 5 years ago, while actually understanding what they're shipping. Pre-AI principles + Post-AI speed is genuinely an undefeated combo. I agree. But the principles have to come first. Principles such as these: 1. Data structures 2. Algorithms 3. System design 4. Database design & normalization 5. Networking (TCP/IP, HTTP, DNS) 6. Operating systems 7. Concurrency & multithreading 8. API design (REST, GraphQL, gRPC) 9. Caching strategies 10. Authentication & authorization 11. Version control (Git, branching strategies) 12. Testing (unit, integration, e2e) 13. CI/CD pipelines 14. Observability (logging, monitoring, tracing) 15. Security fundamentals 16. Design patterns 17. Code review & readability 18. Debugging & profiling 19. Infrastructure basics (containers, orchestration, cloud) 20. Technical communication & documentation These aren't buzzwords to be filled in a resume. These are the things that let you look at AI-generated output and know whether it's production-ready or a liability. AI makes fast engineers faster. But it also makes uninformed engineers more dangerous. The engineer who understands why something works will always outperform the one who just knows that it works. We're all navigating a new world right now. I won't pretend I have it all figured out. But I've been in this industry long enough to recognize an opportunity when I see one. This is a good one. If you spend time on building solid fundamentals and are willing to get genuinely proficient with AI tools (beyond promoting), integrating them into your actual workflow, you can operate at a level that wasn't possible even 2 years ago. Don't waste this window. It won't stay this open forever.

What roles turn a legacy technical team into an AI team that’s ready to deliver value vs. endless PoCs? Just as the AI stack must prioritize value over hype, the AI team’s composition must realign to deliver growth. Data analysts make excellent decision analysts. The focus moves from reporting (BI) with no value to outcomes (AI) with high business and customer impact. Why do business users need data? What outcome or customer value are they trying to deliver? The transition to decision analytics puts the data analyst’s technical skills in line with their business and domain expertise. The result is a high-value role. Data and BI engineers are in the best position to support the business’s emerging information needs. High-value AI is an information product. Decision-makers need information to improve outcomes and create value more efficiently. ML engineers and data scientists have AI engineering skills, so the major shift happening here is from PoCs to products. The product-first mindset and skillset are critical to support AI teams that directly impact the top and bottom line. Product owners and PMs are becoming product strategists and value owners. They ensure that the AI team only works on projects with significant ROI. They shield the AI team from endless PoCs by supporting opportunity discovery and enforcing value-centric prioritization. AI is fundamentally different from prior technologies, so it requires new capabilities and roles. AI Platform Engineers: AI isn’t a standalone technology, so a multi-technology platform is crucial. Agentic Workflow Engineers: Workflows must be reengineered for AI to deliver value. Bolt-on AI doesn’t deliver enough value to justify the costs. Hardware Optimization Engineers: Keeping training and inference costs low is a massive competitive advantage. It makes more use cases economically feasible and delivers higher margins. AI Ops Engineers: AI in production requires constant attention and modification to ensure reliable operation. AI Evaluation & Quality Engineers: Reliability is another massive competitive advantage. AI must work within specific guarantees, or customers won’t pay for it, and internal users won’t adopt it. What roles am I missing (I left one out on purpose)? What is your business doing to transition its legacy technical teams into value-centric AI teams?