Engineering Team Management Skills

Scaling from 50 to 100 employees almost killed our company. Until we discovered a simple org structure that unlocked $100M+ in annual revenue. In my 10+ years of experience as a founder, one of the biggest challenges I faced in scaling was bridging the organizational gap between startup and enterprise. We hit that wall at around 100~ employees. What worked beautifully with a small team suddenly became our biggest obstacle to growth. The problem was our functional org structure: Engineers reporting to engineering, product to product, business to business. This created a complex dependency web: • Planning took weeks • No clear ownership  • Business threw Jira tickets over the fence and prayed for them to get completed • Engineers didn’t understand priorities and worked on problems that didn’t align with customer needs That was when I studied Amazon's Single-Threaded Owner (STO) model, in which dedicated GMs run independent business units with their own cross-functional teams and manage P&L It looked great for Amazon's scale but felt impossible for growing companies like ours. These 2 critical barriers made it impractical for our scale: 1. Engineering Squad Requirements: True STO demands complete engineering teams (including managers) reporting to a single owner. At our size, we couldn't justify full engineering squads for each business unit. To make it work, we would have to quadruple our engineering headcount. 2. P&L Owner Complexity: STO leaders need unicorn-level skills: deep business acumen and P&L management experience. Not only are these leaders rare and expensive, but requiring all these skills in one person would have limited our talent pool and slowed our ability to launch new initiatives. What we needed was a model that captured STO's focus and accountability but worked for our size and growth needs. That's when we created Mission-Aligned Teams (MATs), a hybrid model that changed our execution (for good) Key principles: • Each team owns a specific mission (e.g., improving customer service, optimizing payment flow) • Teams are cross-functional and self-sufficient,  • Leaders can be anyone (engineer, PM, marketer) who's good at execution • People still report functionally for career development • Leaders focus on execution, not people management The results exceeded our highest expectations: New MAT leads launched new products, each generating $5-10M in revenue within a year with under 10 person teams. Planning became streamlined. Ownership became clear. But it's NOT for everyone (like STO wasn’t for us) If you're under 50 people, the overhead probably isn't worth it. If you're Amazon-scale, pure STO might be better. MAT works best in the messy middle: when you're too big for everyone to be in one room but too small for a full enterprise structure. image courtesy of Manu Cornet ------ If you liked this, follow me Henry Shi as I share insights from my journey of building and scaling a  $1B/year business.

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

Lately, there’s been a lot of criticism directed at management on LinkedIn. I firmly believe the best teams need a combination of energy and clarity. At the end of the day - Shit needs to get done! This means you need empowering leaders and clear managers to ensure the team thrives as a whole. The issue often lies in a lack of training, guidance, and examples of what best-in-class looks like. Here’s why a team needs great managers and empowering leaders Management: Persuasion and Direction 1. Clear Communication: Clearly articulate tasks and expectations. Ensure your team understands not just the "what" but the "why" behind their tasks. This builds a sense of purpose and clarity. 2. Structured Approach: Implement structured processes and timelines. Use project management tools to keep everyone on track. Regular check-ins help ensure progress and allow you to address issues promptly. 3. Incentives and Accountability: Establish a system of incentives for meeting goals and holding people accountable when they fall short. Recognition and rewards can motivate, while constructive feedback helps correct course. 4. Empathy and Support: Understand the challenges your team faces and provide the necessary support. This could be resources, training, or simply listening to their concerns. Leadership: Inspiration and Empowerment 1. Vision Casting: Share a compelling vision of the future. People are inspired when they see a bigger picture that they want to be a part of. Communicate this vision regularly and passionately. 2. Empowerment: Empower your team by delegating responsibilities and giving them the autonomy to make decisions. Trusting your team boosts their confidence and drives innovation. 3. Personal Development: Invest in the personal and professional growth of your team members. Encourage them to take on challenges that stretch their capabilities and provide opportunities for learning and advancement. 4. Lead by Example: Demonstrate the behaviors and attitudes you want to see. Your integrity, work ethic, and commitment will inspire others to follow suit. Bridging the Two 1. Balanced Approach: Balance management and leadership by being both directive and inspiring. Adapt your style based on the situation and individual needs. 2. Feedback Loop: Create a feedback loop where your team feels safe to express ideas and concerns. Act on this feedback to improve processes and show that their input is valued. 3. Cultural Alignment: Foster a culture that aligns with both management and leadership principles. Encourage teamwork, innovation, and a shared sense of purpose. 4. Continuous Improvement: Always look for ways to improve both your management and leadership skills. Attend workshops, read extensively, and seek mentorship. By effectively blending management and leadership, you can create a productive, motivated, and high-performing team.

I’ve never met a senior engineer who knows it all. And I’ve never met a junior engineer who had nothing valuable to offer. It’s easy to think that experience solves everything. That after a certain number of years or titles, you’ll “have all the answers.” But real engineering doesn’t work that way. The best senior engineers I know? They still ask questions every day. They still get stumped, learn new things, and rely on others for help. And the best juniors? They bring fresh eyes. They spot the obvious issues seniors sometimes miss. They’re not stuck in “the way things have always been done.” They ask “why?”and sometimes, that’s the question nobody else had the guts to ask. Engineering teams work because we all have blind spots. What you don’t see, someone else will. What you take for granted, someone else will question. So if you’re new, never underestimate your impact. If you’re experienced, never believe you’re above learning from anyone. Every team I’ve seen do great work had one thing in common: Mutual respect, regardless of years, titles, or credentials. There’s always something to learn. There’s always something to teach. That’s how real progress happens.

After 20 years in the Software Engineering field, I can tell who’s a senior, staff, or principal engineer in a meeting by observing their macro and soft skills. The difference isn’t just about technical knowledge or expertise but also about how they present themselves, influence others, and collaborate across the organization. Here’s how they stand apart: 1. Sponsorship     - Principal and Staff Engineers actively support other people’s ideas, even when they would have solved it differently.     - They give credit to others without needing the spotlight, allowing ideas to flourish.     - Confidence in backing bold and innovative solutions shows they value impact over personal gain. 2. Egoless Leadership     - Senior engineers begin learning the importance of admitting what they don’t know and owning their mistakes.     - Staff engineers embrace humility, recognizing that vulnerability builds credibility and trust within their teams.     - Principal engineers embody selfless leadership, understanding that leading isn't about control but enabling others to grow. 3. Openness to Influence     - Great engineers don’t just lead—they also follow when needed.     - They respect others' ownership of projects and provide support, even when they disagree with minor details.     - The best engineers know that alignment, rather than perfection, drives progress in the long term. They ask themselves, “Will this decision still matter to me in six months?” and focus on the bigger picture. 4. Accountability and Ownership     - While Senior Engineers are learning how to drive outcomes and own deliverables, Staff and Principal Engineers master balancing accountability across teams.     - They understand how to align cross-functional efforts to deliver large, strategic projects.     - Their expertise isn't just in delivering solutions but also in navigating ambiguity and breaking down complex problems. 5. Coaching and Mentoring     - Principal and Staff Engineers prioritize mentoring, recognizing that developing others is key to scaling their impact.     - They make space for junior's ideas and actively support their growth.     - A hallmark trait is the ability to listen, guide, and challenge without ego, creating a culture of continuous improvement. If you want to spot the difference, ask these questions:  - Tell me about the last time you supported a junior engineer’s idea.   - What was the worst technical mistake you’ve admitted to your team, and how did you handle it?   - When did someone on your team change your mind, and how did it impact your project? Ultimately, the difference between seniority levels isn’t just technical prowess but a shift in mindset less about personal achievements and more about collective success – P.S: I am starting a paid system design course in a few weeks. It’s suitable for software engineers with 5+ years of experience. Please fill this form if you’re interested: https://lnkd.in/g8E88eMB

The best engineers understand being busy and being effective are different.  I've now written two books about this gap. Leading Effective Engineering Teams came at it from the team and leadership side. The Effective Software Engineer, recently out from O'Reilly, comes at it from the IC side - how to prioritize high-value work and lead beyond your role: https://lnkd.in/gvwDnREU  While these are different audiences, there are four common lessons underneath. 1. Outcomes beat outputs. Every time. Throughput, velocity, how much code has shipped - those are outputs. They're easy to count, which is exactly why we over-index on them. Outcomes are what actually changed for the user or the business. Most struggling teams I've worked with aren't lazy. They're shipping enormous volumes of work nobody needed. Start the month by asking what would have to be true for this work to matter. 2. Efficiency and effectiveness are different problems. Efficiency is doing things right. Effectiveness is doing the right things. Optimize the first without the second and you get very fast movement in the wrong direction. Both books spend real time on the "right things right" spectrum, because you need both - in that order. 3. Name your antipatterns. Each book has a full chapter on them, and it's the chapter readers message me about most. On the IC side: the hero complex, over-engineering, analysis paralysis, gold-plating, the inability to say no. On the team side: last-minute heroics, rubber-stamped reviews, knowledge bottlenecks, retros that never change anything, micromanagement. Cheap to diagnose, expensive to ignore. You can probably spot three on your own team before lunch. 4. Enable, empower, expand. The 3 E's are the backbone of the teams book. Enable people with the context and tooling to do good work. Empower them to decide without waiting on you. Then expand that capability beyond your own team. It's the closest thing I have to a general theory of scaling yourself - and it works for ICs too. Leading without authority is the same motion at a smaller radius. The thread running through all ~550 pages: careers are marathons, not sprints. Effectiveness compounds. Heroics don't. Both books are out now from O'Reilly. I hope there’s something in here that's helpful :) #programming #softwareengineering 

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?

Teams will increasingly include both humans and AI agents. We need to learn how best to configure them. A new Stanford University paper "ChatCollab: Exploring Collaboration Between Humans and AI Agents in Software Teams" reveals a range of useful insights. A few highlights: 💡 Human-AI Role Differentiation Fosters Collaboration. Assigning distinct roles to AI agents and humans in teams, such as CEO, Product Manager, and Developer, mirrors traditional team dynamics. This structure helps define responsibilities, ensures alignment with workflows, and allows humans to seamlessly integrate by adopting any role. This fosters a peer-like collaboration environment where humans can both guide and learn from AI agents. 🎯 Prompts Shape Team Interaction Styles. The configuration of AI agent prompts significantly influences collaboration dynamics. For example, emphasizing "asking for opinions" in prompts increased such interactions by 600%. This demonstrates that thoughtfully designed role-specific and behavioral prompts can fine-tune team dynamics, enabling targeted improvements in communication and decision-making efficiency. 🔄 Iterative Feedback Mechanisms Improve Team Performance. Human team members in roles such as clients or supervisors can provide real-time feedback to AI agents. This iterative process ensures agents refine their output, ask pertinent questions, and follow expected workflows. Such interaction not only improves project outcomes but also builds trust and adaptability in mixed teams. 🌟 Autonomy Balances Initiative and Dependence. ChatCollab’s AI agents exhibit autonomy by independently deciding when to act or wait based on their roles. For example, developers wait for PRDs before coding, avoiding redundant work. Ensuring that agents understand role-specific dependencies and workflows optimizes productivity while maintaining alignment with human expectations. 📊 Tailored Role Assignments Enhance Human Learning. Humans in teams can act as coaches, mentors, or peers to AI agents. This dynamic enables human participants to refine leadership and communication skills, while AI agents serve as practice partners or mentees. Configuring teams to simulate these dynamics provides dual benefits: skill development for humans and improved agent outputs through feedback. 🔍 Measurable Dynamics Enable Continuous Improvement. Collaboration analysis using frameworks like Bales’ Interaction Process reveals actionable patterns in human-AI interactions. For example, tracking increases in opinion-sharing and other key metrics allows iterative configuration and optimization of combined teams. 💬 Transparent Communication Channels Empower Humans. Using shared platforms like Slack for all human and AI interactions ensures transparency and inclusivity. Humans can easily observe agent reasoning and intervene when necessary, while agents remain responsive to human queries. Link to paper in comments.

I used to think Customer Support teams belonged under the CCO. But now my preference is for support to report into the CTO. Why? 1. Tighter collaboration between support and engineering Engineering and support leadership are in the same meetings and discussions on the day-to-day. So they get more “voice of customer” on a daily basis and they aren’t at arms-length to issues and customer feedback. Powerful. 2. End-to-end process ownership Beginning with customer-reported issues, triage, technical escalation, to resolution, the full span of control for issue resolution resides beneath one executive. 3. Commercial focus Allows the CCO / CRO to be more focused on revenue operations vs support operations. I’ve led support multiple times in my career as a CCO / SVP, Success. For execs who need to focus on retention and growth, this is an unnecessary drag on time and resources that are better spent driving customer outcomes and revenue. Wherever it lives, communication and collaboration with other customer-facing teams is critical. What do you think? Will this work in your company? Why or why not? — 🎉 Happy New Year! The CustomerSuccess[dot]io newsletter will be back Sunday, Jan 7. Click “visit my website” above to join. #customersuccess #saas #cco #cro #support Jay Nathan

If you’re in leadership, you need to understand *how* genAI will transform your organization, and what that means for restructuring teams. Here's what we're learning: BREAKTHROUGH IN AI IDEATION OpenAI is getting ready to launch new AI models (o3 and o4-mini) that can connect concepts across different disciplines ranging from nuclear fusion to pathogen detection. (Reporting from The Information's Stephanie Palazzolo and Amir Efrati). Molecular biologist Sarah Owens used the system to design a study applying ecological techniques to pathogen detection and said doing this without AI "would have taken days." THE NEW TEAMMATE EMERGES Remember the HBS study with 776 Procter & Gamble professionals? It showed that genAI functioned as an actual teammate. Individuals using AI performed at levels comparable to traditional human teams, achieving a 37% performance improvement over solo workers without AI. Teams using AI were three times more likely to produce top-quality solutions while completing tasks 12.7% faster and producing more detailed outputs. BREAKING DOWN SILOS That study showed that AI also dissolves professional boundaries. Without AI, R&D specialists created technical solutions while Commercial specialists developed market-focused ideas. With AI, both types of specialists produced balanced solutions integrating technical and commercial perspectives. A NEW KIND OF TEAM AI users reported higher levels of excitement and enthusiasm while experiencing less anxiety and frustration. Individuals working alone with AI reported emotional experiences comparable to those in human teams. That's wild. RESTRUCTURING FOR ADVANTAGE The HBS study showed that AI reduces dominance effects in team collaboration. When genAI translates between roles, it accelerates iteration at a pace that there’s no way traditional teams could match. ++++++++++++++++++++ THREE THINGS YOU SHOULD BE DOING NOW: 1. Upskill your entire workforce: Develop a fundamental behavioral shift in how teams interact with AI across every task. This only works if everyone is doing it. (We work with enterprise to upskill at scale - more below.) 2. Experiment with new team structures: Test different AI-team combinations. Try individuals with AI for routine tasks and small teams with AI for complex challenges. Find what works best for your specific needs. 3. Redefine success metrics: Set new standards for what good work looks like with AI. Track not just productivity but also idea quality, knowledge sharing across departments, and team satisfaction—all areas where AI shows major benefits. ++++++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your company is ready, we are ready to upskill your workforce at scale. Our Generative AI for Professionals course is tailored to enterprise and highly effective in driving AI adoption through a unique, proven behavioral transformation. It's pretty awesome. Check out our website or shoot me a DM.