In 2024 and beyond, engineering teams face a unique convergence of pressures: faster release cycles, distributed workforces, increasingly complex tech stacks, and the rapid adoption of AI-assisted coding tools like GitHub Copilot. Amid this complexity, the tech lead has emerged as the critical role that bridges high-level engineering strategy with day-to-day delivery outcomes. Without effective technical leadership, even the most talented development teams struggle to ship quality software consistently.
This article focuses on the practical responsibilities of a technical lead within Scrum, Kanban, and SAFe-style agile environments. We’re writing from the perspective of Typo, an engineering analytics platform that works closely with VPs of Engineering, Directors, and Engineering Managers who rely on Tech Leads to translate strategy and data into working software. Our goal is to give you a concrete responsibility map for the tech lead role, along with examples of how to measure impact using engineering metrics like DORA, PR analytics, and cycle time.
Here’s what we’ll cover in this guide:
A technical lead is a senior software engineer who is accountable for the technical direction, code quality, and mentoring within their team—while still actively writing code themselves. Unlike a pure manager or architect who operates at a distance, the Tech Lead stays embedded in the codebase, participating in code reviews, pairing with developers, and making hands-on technical decisions daily.
While the Technical Lead role is not explicitly defined in Scrum, it is commonly found in many software teams. The shift from roles to accountabilities in Scrum has allowed for the integration of Technical Leads without formal recognition in the Scrum Guide.
It’s important to recognize that “Tech Lead” is a role, not necessarily a job title. In many organizations, a Staff Engineer, Principal Engineer, or even a Senior Engineer may act as the TL for a squad or pod. The responsibilities remain consistent regardless of what appears on the org chart.
How this role fits into common agile frameworks varies slightly:
Let’s be explicit about what a Tech Lead is not:
Typical characteristics of a Tech Lead include:
Tech Leads must balance hands-on engineering—often spending 40-60% of their time writing code—with technical decision-making, risk management, and quality stewardship. This section breaks down the core technical responsibilities that define the role.
Key responsibilities of a Technical Lead include defining the technical direction, ensuring code quality, removing technical blockers, and mentoring developers. Technical Leads define the technical approach, select tools/frameworks, and enforce engineering standards for maintainable code. They are responsible for establishing coding standards and leading code review processes to maintain a healthy codebase. The Tech Lead is responsible for guiding architectural decisions and championing quality within the team.
Architecture and Design
The tech lead is responsible for shaping and communicating the team’s architecture, ensuring it aligns with broader platform direction and meets non-functional requirements around performance, security, and scalability. This doesn’t mean dictating every design decision from above. In self organizing teams, architecture should emerge from collective input, with the TL facilitating discussions and providing architectural direction when the team needs guidance.
For example, consider a team migrating from a monolith to a modular services architecture over 2023-2025. The Tech Lead would define the migration strategy, establish boundaries between services, create patterns for inter-service communication, and mentor developers through the transition—all while ensuring the entire team understands the rationale and can contribute to design decisions.
Technical Decision-Making
Tech Leads own or convene decisions on frameworks, libraries, patterns, and infrastructure choices. Rather than making these calls unilaterally, effective TLs use lightweight documentation like Architecture Decision Records (ADRs) to capture context, options considered, and rationale. This creates transparency and helps developers understand why certain technical decisions were made.
The TL acts as a feasibility expert, helping the product owner understand what’s technically possible within constraints. When a new feature request arrives, the Tech Lead can quickly assess complexity, identify risks, and suggest alternatives that achieve the same business outcome with less technical implementation effort.
Code Quality and Standards
A great tech lead sets and evolves coding standards, code review guidelines, branching strategies, and testing practices for the team. This includes defining minimum test coverage requirements, establishing CI rules that prevent broken builds from merging, and creating review checklists that ensure consistent code quality across the codebase.
Modern Tech Leads increasingly integrate AI code review tools into their workflows. Platforms like Typo can track code health over time, helping TLs identify trends in code quality, spot hotspots where defects cluster, and ensure that experienced developers and newcomers alike maintain consistent standards.
Technical Debt Management
Technical debt accumulates in every codebase. The Tech Lead’s job is to identify, quantify, and prioritize this debt in the product backlog, then negotiate with the product owner for consistent investment in paying it down. Many mature teams dedicate 10-20% of sprint capacity to technical debt reduction, infrastructure improvements, and automation.
Without a TL advocating for this work, technical debt tends to accumulate until it significantly slows feature development. The Tech Lead translates technical concerns into business terms that stakeholders can understand—explaining, for example, that addressing authentication debt now will reduce security incident risk and cut feature development time by 30% in Q3.
Security and Reliability
Tech Leads partner with SRE and Security teams to ensure secure-by-default patterns, resilient architectures, and alignment with operational SLIs and SLOs. They’re responsible for ensuring the team understands security best practices, that code reviews include security considerations, and that architectural choices support reliability goals.
This responsibility extends to incident response. When production issues occur, the Tech Lead often helps identify the root cause, coordinates the technical response, and ensures the team conducts blameless postmortems that lead to genuine improvements rather than blame.
Tech Leads are critical to turning product intent into working software within short iterations without burning out the team. While the development process can feel chaotic without clear technical guidance, a skilled TL creates the structure and clarity that enables consistent delivery.
Partnering with Product Owners / Product Managers
The Tech Lead works closely with the product owner during backlog refinement, helping to slice user stories into deliverable chunks, estimate technical complexity, and surface dependencies and risks early. When the Product Owner proposes a feature, the TL can quickly assess whether it’s feasible, identify technical prerequisites, and suggest acceptance criteria that ensure the implementation meets both business and technical requirements.
This partnership is collaborative, not adversarial. The Product Owner owns what gets built and in what priority; the Tech Lead ensures the team understands how to build it sustainably. Neither can write user stories effectively without input from the other.
Working with Scrum Masters / Agile Coaches
The scrum master role focuses on optimizing process and removing organizational impediments. The Tech Lead, by contrast, optimizes technical flow and removes engineering blockers. These responsibilities complement each other without overlapping.
In practice, this means the TL and Scrum Master collaborate during ceremonies. In sprint planning, the TL helps the team break down work technically while the Scrum Master ensures the process runs smoothly. In retrospectives, both surface different types of impediments—the Scrum Master might identify communication breakdowns while the Tech Lead highlights flaky tests slowing the agile process.
Sprint and Iteration Planning
The Tech Lead helps the team break down initiatives into deliverable slices, set realistic commitments based on team velocity, and avoid overcommitting. This requires understanding both the technical work involved and the team’s historical performance.
Effective TLs push back when plans are unrealistic. If leadership wants to hit an aggressive sprint goal, the Tech Lead can present data showing that the team’s average velocity makes the commitment unlikely, then propose alternatives that balance ambition with sustainability.
Cross-Functional Collaboration
Modern software development requires collaboration across disciplines. The Tech Lead coordinates with the UX designer on technical constraints that affect interface decisions, works with Data teams on analytics integration, partners with Security on compliance requirements, and collaborates with Operations on deployment and monitoring.
For example, launching a new AI-based recommendation engine might involve the TL coordinating across multiple teams: working with Data Science on model integration, Platform on infrastructure scaling, Security on data privacy requirements, and Product on feature rollout strategy.
Stakeholder Communication
Tech Leads translate technical trade-offs into business language for engineering managers, product leaders, and sometimes customers. When a deadline is at risk, the TL can explain why in terms stakeholders understand—not “we have flaky integration tests” but “our current automation gaps mean we need an extra week to ship with confidence.”
This communication responsibility becomes especially critical under tight deadlines. The TL serves as a bridge between the team’s technical reality and stakeholder expectations, ensuring both sides have accurate information to make good decisions.
Effective Tech Leads are multipliers. Their main leverage comes from improving the whole team’s capability, not just their own individual contributor output. A TL who ships great code but doesn’t elevate team members is only half-effective.
Mentoring and Skill Development
Tech Leads provide structured mentorship for junior and mid-level developers on the team. This includes pair programming sessions on complex problems, design review discussions that teach architectural thinking, and creating learning plans for skill gaps the team needs to close.
Mentoring isn’t just about technical skills. TLs also help developers understand how to scope work effectively, how to communicate technical concepts to non-technical stakeholders, and how to navigate ambiguity in requirements.
Feedback and Coaching
Great TLs give actionable feedback constantly—on pull requests, design documents, incident postmortems, and day-to-day interactions. The goal is continuous improvement, not criticism. Feedback should be specific (“this function could be extracted for reusability”) rather than vague (“this code needs work”).
An agile coach might help with broader process improvements, but the Tech Lead provides the technical coaching that helps individual developers grow their engineering skills. This includes answering questions thoughtfully, explaining the “why” behind recommendations, and celebrating when team members demonstrate growth.
Enabling Ownership and Autonomy
A new tech lead often makes the mistake of trying to own too much personally. Mature TLs delegate ownership of components or features to other developers, empowering them to make decisions and learn from the results. The TL’s job is to create guardrails and provide guidance, not to become a gatekeeper for every change.
This means resisting the urge to be the hero coder who solves every hard problem. Instead, the TL should ask: “Who on the team could grow by owning this challenge?” and then provide the support they need to succeed.
Psychological Safety and Culture
The Tech Lead models the culture they want to create. This includes leading blameless postmortems where the focus is on systemic improvements rather than individual blame, maintaining a respectful tone in code reviews, and ensuring all team members feel included in technical discussions.
When a junior developer makes a mistake that causes an incident, the TL’s response sets the tone for the entire team. A blame-focused response creates fear; a learning-focused response creates safety. The best TLs use failures as opportunities to improve both systems and skills.
Team Health Signals
Modern Tech Leads use engineering intelligence tools to monitor signals that indicate team well-being. Metrics like PR review wait time, cycle time, interruption frequency, and on-call burden serve as proxies for how the team is actually doing.
Platforms like Typo can surface these signals automatically, helping TLs identify when a team builds toward burnout before it becomes a crisis. If one developer’s review wait times spike, it might indicate they’re overloaded. If cycle time increases across the board, it might signal technical debt or process problems slowing everyone down.
Modern Tech Leads increasingly rely on metrics to steer continuous improvement. This isn’t about micromanagement—it’s about having objective data to inform decisions, spot problems early, and demonstrate impact over time.
The shift toward data-driven technical leadership reflects a broader trend in engineering. Just as product teams use analytics to understand user behavior, engineering teams can use delivery and quality metrics to understand their own performance and identify opportunities for improvement.
Flow and Delivery Metrics
DORA metrics have become the standard for measuring software delivery performance:
Beyond DORA, classic SDLC metrics like cycle time (from work started to work completed), work-in-progress limits, and throughput help TLs understand where work gets stuck and how to improve flow.
Code-Level Metrics
Tech Leads should monitor practical signals that indicate code health:
These metrics help TLs make informed decisions about where to invest in code quality improvements. If one module shows high defect density and frequent changes, it’s a candidate for dedicated refactoring efforts.
Developer Experience Metrics
Engineering output depends on developer well-being. TLs should track:
These qualitative and quantitative signals help TLs understand friction in the development process that pure output metrics might miss.
How Typo Supports Tech Leads
Typo consolidates data from GitHub, GitLab, Jira, CI/CD pipelines, and AI coding tools to give Tech Leads real-time visibility into bottlenecks, quality issues, and the impact of changes. Instead of manually correlating data across tools, TLs can see the complete picture in one place.
Specific use cases include:
Data-Informed Coaching
Armed with these insights, Tech Leads can make 1:1s and retrospectives more productive. Instead of relying on gut feel, they can point to specific data: “Our cycle time increased 40% last sprint—let’s dig into why” or “PR review latency has dropped since we added a second reviewer—great job, team.”
This data-informed approach focuses conversations on systemic fixes—process, tooling, patterns—rather than blaming individuals. The goal is always continuous improvement, not surveillance.
Every Tech Lead wrestles with the classic tension: code enough to stay credible and informed, but lead enough to unblock and grow the team. There’s no universal formula, but there are patterns that help.
Time Allocation
Most Tech Leads find a 50/50 split between coding and leadership activities works as a baseline. In practice, this balance shifts constantly:
The key is intentionality. TLs should consciously decide where to invest time each week rather than just reacting to whatever’s urgent.
Avoiding Bottlenecks
Anti-patterns to watch for:
Healthy patterns include enabling multiple reviewers with merge authority, documenting decisions so other developers understand the rationale, and deliberately building shared ownership of complex systems.
Choosing What to Code
Not all coding work is equal for a Tech Lead. Prioritize:
Delegate straightforward tasks that provide good growth opportunities for other developers. The goal is maximum leverage, not maximum personal output.
Communication Rhythms
Daily and weekly practices help TLs stay connected without micromanaging:
These rhythms create structure without requiring the TL to be in every conversation.
Personal Sustainability
Learn how DORA metrics can help improve software delivery performance and efficiency.
Tech Leads wear many hats, and it’s easy to burn out. Protect yourself by:
A burned-out TL can’t effectively lead. Sustainable pace matters for the person in the role, not just the team they lead.
The Tech Lead role looks different at a 10-person startup versus a 500-person engineering organization. Understanding how responsibilities evolve helps TLs grow their careers and helps leaders build effective high performing teams at scale.
From Single-Team TL to Area/Tribe Lead
As organizations grow, some Tech Leads transition from leading a single squad to coordinating multiple teams. This shift involves:
For example, a TL who led a single payments team might become a “Technical Area Lead” responsible for the entire payments domain, coordinating three squads with their own TLs.
Interaction with Staff/Principal Engineers
In larger organizations, Staff and Principal Engineers define cross-team architecture and long-term technical vision. Tech Leads collaborate with these senior ICs, implementing their guidance within their teams while providing ground-level feedback on what’s working and what isn’t.
This relationship should be collaborative, not hierarchical. The Staff Engineer brings breadth of vision; the Tech Lead brings depth of context about their specific team and domain.
Governance and Standards: Learn how integrating development tools can support governance and enforce engineering standards across your team.
As organizations scale, governance structures emerge to maintain consistency:
Tech Leads participate in and contribute to these forums, representing their team’s perspective while aligning with broader organizational direction.
Hiring and Onboarding
Tech Leads typically get involved in hiring:
Once new engineers join, the TL leads their technical onboarding—introducing them to the tech stack, codebase conventions, development practices, and ongoing projects.
Measuring Maturity
TLs can track improvement over quarters using engineering analytics. Trend lines for cycle time, defect rate, and deployment frequency show whether leadership decisions are paying off. If cycle time drops 25% over two quarters after implementing PR size limits, that’s concrete evidence of effective technical leadership.
For example, when spinning up a new AI feature squad in 2025, an organization might assign an experienced TL, then track metrics from day one to measure how quickly the team reaches productive velocity compared to previous team launches.
Tech Leads need clear visibility into delivery, quality, and developer experience to make better decisions. Without data, they’re operating on intuition and incomplete information. Typo provides the view that transforms guesswork into confident leadership.
SDLC Visibility
Typo connects Git, CI, and issue trackers to give Tech Leads end-to-end visibility from ticket to deployment. You can see where work is stuck—whether it’s waiting for code review, blocked by failed tests, or sitting in a deployment queue. This visibility helps TLs intervene early before small delays become major blockers.
AI Code Impact and Code Reviews
As teams adopt AI coding tools like Copilot, questions arise about impact on quality. Typo can highlight how AI-generated code affects defects, review time, and rework rates. This helps TLs tune their team’s practices—perhaps AI-generated code needs additional review scrutiny, or perhaps it’s actually reducing defects in certain areas.
Delivery Forecasting
Stop promising dates based on optimism. Typo’s delivery signals help Tech Leads provide more reliable timelines to Product and Leadership based on historical performance data. When asked “when will this epic ship?”, you can answer with confidence rooted in your team’s actual velocity.
Developer Experience Insights
Developer surveys and behavioral signals help TLs understand burnout risks, onboarding friction, and process pain points. If new engineers are taking twice as long as expected to reach full productivity, that’s a signal to invest in better documentation or mentoring practices.
If you’re a Tech Lead or engineering leader looking to improve your team’s delivery speed and quality, Typo can give you the visibility you need. Start a free trial to see how engineering analytics can amplify your technical leadership—or book a demo to explore how Typo fits your team’s specific needs.
The tech lead role sits at the intersection of deep technical expertise and team leadership. In agile environments, this means balancing hands-on engineering with mentoring, architecture with collaboration, and personal contribution with team multiplication.
With clear responsibilities, the right practices, and data-driven visibility into delivery and quality, Tech Leads become the force multipliers that turn engineering strategy into shipped software. The teams that invest in strong technical leadership—and give their TLs the tools to see what’s actually happening—consistently outperform those that don’t.
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