Engineering Manager, Fired at 48, Is the Boss to Blame?

Summary

Dan recounts being fired after about two years as an engineering manager. He describes how a strained relationship with a new VP of Engineering, disagreements about delivery forecasts, and changing performance-management practices affected his work.

He emphasizes that this is his perspective and may be shaped by personal bias; he does not claim to know the full reasons for the company’s decisions.

Management and organizational dynamics

• Hiring a senior leader: Dan interviewed candidates for a new VP of Engineering role using standardized questions. He wanted a leader who could provide coaching and support, help him think through difficult team decisions, and avoid micromanaging. He felt his input may have been largely a formality because senior leadership had final authority.
• Expectations and trust: Dan says an early disagreement with the new VP over a delivery forecast became a turning point. He felt the VP questioned his knowledge of his team’s capacity, despite available delivery data. After that, Dan says he stopped trusting the VP and limited their one-on-ones largely to status updates.
• Performance feedback: Dan reports receiving ratings of 2 out of 5 for issues including insufficient use of AI and forecasts the VP considered inadequate. He also says criticism sometimes arose around performance-review periods, while other weekly one-on-ones were less contentious.
• Psychological safety: Dan says he was criticized for not attending retrospectives, which the VP treated as a sign that his team lacked psychological safety. Dan disagreed, saying he had previously attended retrospectives and believed he had built trust with his teams.
• Potential organizational pressure: Dan observed a push for managers to have harder performance conversations and what he interpreted as informal stack-ranking. He also saw high-performing colleagues let go, with explanations he considered unclear, and no apparent backfills. He speculates that the cuts could be related to recovering AI spending costs, but presents this as a possibility rather than an established fact.
• Retention and career context: Dan describes a pattern of long tenures earlier in his career, followed by shorter stays at recent software companies. His latest role lasted about two years; a prior role at a publicly traded software company lasted almost three years.

Processes, frameworks, and operational practices

Structured hiring interviews

Dan’s approach to interviewing included:

• Using prepared, consistent questions across candidates.
• Explaining the question, the kind of response he was seeking, and the competency he was assessing, rather than trying to “catch” candidates out.
• Assessing leadership qualities such as coaching, support, culture fit, and autonomy without micromanagement.

Delivery forecasting with Jira and ActionableAgile

The company tracked work in Jira and used the ActionableAgile plugin to forecast delivery with Monte Carlo simulations, producing estimated delivery windows with confidence ranges.

Dan reported project forecasts weekly. He says disagreements with Product often arose when stakeholders disliked the forecast. His reflection is that when a faster delivery date is needed, teams should be able to discuss scope trade-offs rather than treat the forecast as the only variable. He felt scope was often not negotiable in his organization.

Manager feedback and team signals

Dan describes weekly one-on-ones and periodic lightweight performance reviews, but says feedback felt inconsistent and sometimes poorly timed. His experience suggests that performance feedback should be specific and consistent, and should not come as a surprise during high-pressure conversations.

He argues that managers need enough trust to raise concerns and ask for support. Once trust erodes, the working relationship may deteriorate toward departure, transfer, or termination.

Metrics and timelines mentioned

• Experience: About 20 years in tech, much of it in leadership.
• Tenure in the latest role: About two years; fired roughly three months before recording.
• Forecasting: Project forecasts reported weekly.
• Performance feedback: Ratings of 2/5 were cited for concerns including AI usage and forecasting. Dan describes a strained period lasting at least six months.
• Career history: Approximately five years at McDonald’s, six years at Comp USA, seven or eight years at a managed-services provider, and seven years at another employer; nearly three years at a later public software company.
• Financial metrics: No revenue, margin, CAC, LTV, or other company financial KPIs are provided. The company’s AI-related cost pressure is Dan’s speculation, not a confirmed metric or rationale.

Practical takeaways

• Make delivery estimates transparent, but pair forecasts with explicit discussion of scope, capacity, and trade-offs.
• In senior-leader hiring, assess not only technical competence but also coaching style, communication, and willingness to empower managers.
• Give managers clear, consistent performance expectations. Avoid relying on ambiguous signals, such as retrospective attendance, as a proxy for psychological safety.
• Treat trust as an operational requirement: address disagreements directly and early, before one-on-ones become purely transactional.
• When workforce reductions or AI-related changes are suspected, distinguish verified explanations from employee interpretation.

Presenter/source: Dan, speaking from his personal experience as a former engineering manager. Tools and systems mentioned: Jira, ActionableAgile, Monte Carlo forecasting, and Claude.