Engineering Management — Page 3 of 7
Browsing page 3 of 7 — 79 articles on engineering management.
Building a Technology Radar for Your Engineering Team: Evaluating Tools, Managing Adoption Risk, and Aligning Technical Direction
A practical guide for startup CTOs and engineering leaders on building and running a ThoughtWorks-style technology radar: the four rings, the four quadrants, how to facilitate quarterly sessions, and how to document decisions that stick.
Running API Design Reviews: Contracts, Backward Compatibility, and Change Management
A practical guide to establishing API design reviews as a team engineering practice, covering proposal docs, approval gates, OpenAPI contract-first development, automated compatibility checking in CI, and scaling the process from a 5-person team to a platform org.
Engineering Knowledge Management: Building Internal Documentation Systems That Engineers Actually Use
Most internal documentation fails not because engineers can't write, but because the system has no ownership model, no freshness signals, and no feedback loop. This guide covers documentation architecture, tooling tradeoffs, staleness detection, health metrics, and the cultural practices that make documentation stick.
Managing External Engineering Partners: Contracts, Code Quality Gates, and Knowledge Transfer for Startup CTOs
A practical guide for startup CTOs on managing engineering agencies, contractors, and fractional teams — from contract structures and IP ownership to code quality gates, knowledge transfer protocols, and measuring partner effectiveness.
Technical Due Diligence for AI-Generated Codebases: Assessment Frameworks, Risk Scoring, and Remediation Roadmaps for Investors and CTOs
A systematic framework for evaluating codebases partially or fully generated by AI tools, including security audit methodology, architecture review, risk scoring, and realistic remediation timelines.
Engineering Productivity Metrics That Actually Matter: DORA, SPACE, and What to Measure Without Destroying Trust
A practical guide for engineering leaders on measuring team productivity without creating perverse incentives. Covers DORA, the SPACE framework, why lines of code and story points are harmful proxies, and how to build a metrics dashboard your team actually trusts.
Governing AI Agents in Your Engineering Organization: Shadow AI Detection, Sanctioned Workflows, and Risk Frameworks for CTOs
A practical guide for startup CTOs dealing with the shadow AI problem: how to detect ungoverned AI usage, build a sanctioned tooling stack, and create a governance framework that moves at startup speed without creating bureaucratic overhead.
How to Evaluate an Engineering Agency: The Questions, Red Flags, and Decision Framework CTOs Use in 2026
A practical framework for CTOs and startup founders evaluating engineering agencies in 2026, covering AI-readiness, senior talent ratios, KPI-linked delivery, debt management, and the red flags that disqualify an agency before the conversation starts.
Engineering OKRs That Drive Results: Setting Technical Goals, Measuring Impact, and Avoiding Vanity Metrics
Most engineering OKRs measure activity instead of outcomes. This guide covers how to write measurable technical objectives, align them with business goals, and run a review cadence that actually works.
Hiring Your First Engineer: What Startup CTOs Get Wrong
A practical guide for seed-stage CTOs on the structural mistakes that kill first engineering hires: pedigree over pragmatism, seniority miscalibration, undefined scope, and broken technical assessment. Includes compensation frameworks and a hire-vs-outsource decision matrix.
Incident Communication for Engineering Leaders: Status Pages, Stakeholder Updates, and Building Trust During Outages
How engineering leaders should handle communication during incidents: status page design, internal vs external cadence, stakeholder update templates, post-incident transparency, and the communication mistakes that permanently erode trust.
Engineering Organization Design for AI-Augmented Teams: Roles, Workflows, and Productivity When AI Generates 40% of Your Code
When AI coding tools generate a significant share of your team's output, the organizational model that got you here stops working. This is a practical guide to restructuring roles, workflows, hiring, and metrics for teams where AI is a real contributor.