How to Hire Software Engineers: Personality Traits, Interview Questions and Assessment
Go beyond coding tests. Which Big Five traits predict engineering team success, behavioral interview questions, and free personality assessment tools.
Preview an interview kit
The Real Challenge of Hiring Software Engineers
Software engineering teams face unprecedented churn and cost pressures that traditional hiring methods fail to address. Industry benchmarks report annual attrition of 12–13 percent overall and nearly 20 percent for junior engineers, translating to an average replacement cost of at least $240,000 per mis-hire and up to $625,000 in complex environments. Meanwhile, a global study shows an average time-to-fill of 49 days and roughly four months before a new hire reaches full productivity. With only 16 percent of an engineer’s time devoted to net-new code and the rest absorbed by meetings, maintenance, and tool friction, the stakes for getting hiring right could not be higher.
Relying on resumes and unstructured interviews only compounds these challenges. Unstructured interviews correlate with job performance at a mere r ≈ .19, whereas structured approaches double that predictive validity to r ≈ .42. Recruiters sift through hundreds of applicants—60 percent of whom fail basic technical screens—without reliable metrics to gauge collaboration traits or stress resilience. In an environment where code reliability, rapid onboarding, and cross-team communication drive product velocity, a more systematic, trait-based approach is essential to reduce noise and ensure sustainable team throughput.
Personality Traits That Predict Software Engineer Success
Conscientiousness
High Conscientiousness correlates with version-control discipline, test coverage follow-through, and sprint predictability, with validity coefficients around r ≈ .20–.22 for overall performance (Sackett et al., 2021). Engineers scoring in the top 25th percentile reliably deliver on release schedules and adhere to coding standards, reducing regression risk by up to 30 percent in some internal audits. Their structured approach to bug tracking and peer-review workflows accelerates cycle time by minimizing rework. Hiring for Conscientiousness ensures code integrity and predictable sprint outcomes.
Openness to Experience
Moderate-High Openness fosters rapid adaptation to emerging frameworks and exploratory prototyping without sacrificing stability, with meta-analytic links to innovative contributions (r ≈ .15). Developers in the 60th to 85th percentile embrace new libraries and toolchains effectively, reducing onboarding time on novel stacks by 20–25 percent. However, scores above the 90th can drift into ‘shiny-object’ syndrome, so target a balanced range. This calibrated Openness promotes creativity while maintaining focus on deliverables.
Agreeableness
Moderate Agreeableness is crucial for healthy DevOps collaboration, buffering teams under uncertainty while still permitting robust debate. Data from agent-based modeling shows that too low Agreeableness spikes negative code-review interactions by 40 percent, whereas very high scores may dampen constructive push-back on risky architecture decisions. Engineers in the midrange foster respectful feedback loops and maintain sprint morale. Prioritizing this trait smooths handoffs between front-end, back-end, and QA teams.
Extraversion
Low to Moderate Extraversion aligns with the deep-work demands of coding, showing a modest positive link to individual performance (r ≈ .14) but providing critical energy in pair-programming and technical demos. Candidates comfortable speaking up in design sessions without constant social stimulation strike the right balance between focus and communication. Excessive Extraversion can distract from individual contributor tasks, while too little may hinder stakeholder discussions. Targeting that mid-level helps engineers articulate complex ideas without sacrificing concentration.
Emotional Stability (Low Neuroticism)
Low Neuroticism predicts superior incident-response quality and resilience under pressure, with correlations around r ≈ –.15 for stress-induced error escalation. Emotionally stable engineers remain composed during on-call rotations and critical production incidents, reducing mean time to recovery by up to 20 percent in high-voltage environments. Teams with lower average Neuroticism also report 15 percent less burnout over six months. Emphasizing stability decreases costly downtime and preserves team well-being.
What the Research Actually Shows
A robust body of meta-analytic research dating back to Barrick & Mount (1991) and Schmidt & Hunter (1998), and reaffirmed by Sackett et al. (2021), demonstrates that Big Five personality traits explain incremental variance in job performance beyond cognitive ability alone. Conscientiousness, in particular, consistently emerges as a top predictor, while appropriate levels of Openness, Agreeableness, Extraversion, and Emotional Stability each contribute uniquely to dimensions of software-engineering success—from code quality and sprint reliability to collaborative problem-solving and stress resilience.
Critically, structured assessment methods amplify these trait signals. Standardized personality instruments, when combined with structured behavioral interviews, yield predictive validities (r ≈ .42) that far exceed unstructured approaches (r ≈ .19). Incorporating work samples alongside personality data further boosts validity to as high as r ≈ .54, according to Master-HR insights. By layering quantitative trait scores, technical assessments, and calibrated interview rubrics, hiring teams can make defensible, data-driven decisions rather than relying on gut feel or ad-hoc impressions.
Research Spotlight
A meta-analysis by Schmidt & Hunter (1998) showed that structured interviews have a validity coefficient of r ≈ .42 for predicting job performance, more than double the r ≈ .19 observed for unstructured interviews (Master-HR, 2023). This finding underscores the dramatic impact that standardized questions, consistent scoring, and calibrated follow-up probes can have on hiring outcomes in software engineering.
Interview Questions That Actually Predict Performance
Behavioral interview questions rooted in past work experiences yield far greater predictive power than hypothetical or brain-teaser inquiries. By aligning each question to a specific Big Five trait—like asking about defect management for Conscientiousness or crisis response for Emotional Stability—you can systematically assess how candidates have navigated real-world challenges. Using a 1–5 scoring rubric, where 5 signals structured processes, metrics, and reflective learning, ensures consistency across interviewers and minimizes subjective bias.
Well-designed questions also double as developmental probes. When an engineer recounts how they mediated a heated code-review thread, you’re not just measuring Agreeableness—you’re teaching your team what healthy conflict resolution looks like. Calibration meetings before live interviews help interviewers norm on what constitutes a ‘3’ versus a ‘5,’ creating a shared language around performance expectations and improving inter-rater reliability.
Behavioral Interview Questions with Scoring Guidance
Walk me through the last time you owned a release candidate with zero critical defects. How did you ensure quality?
Strong (5) candidates describe explicit checklists, peer-review gates, automated test coverage metrics, and post-release retrospectives that fed continuous improvement. They cite concrete defect-rate reductions or sprint velocity gains. A red flag (1) answer evades specifics or shifts blame to QA teams. This question targets Conscientiousness by revealing discipline in release management.
Tell me about a time you noticed missing acceptance criteria late in a sprint. What did you do?
Top performers (5) detail immediate triage steps—updating requirements in the backlog, rallying stakeholders for scope alignment, and logging lessons learned. They demonstrate clear communication and risk mitigation. A minimal response (1) glosses over stakeholder coordination or admits to ignoring the gap. This probe measures Conscientiousness in high-pressure scenarios.
Describe a situation where you had to learn a new language or framework in days, not weeks. How did you approach it?
An exemplary (5) response outlines a structured learning plan: tutorial breakdown, pairing with a mentor, rapid prototyping, and knowledge-share sessions. Candidates share metrics on reduced ramp-up time. A poor answer (1) confesses to trial-and-error without strategic planning. This question assesses Openness to Experience and learning agility.
Have you ever advocated against adopting a trendy tool? What data did you use?
High scorers present usage metrics, performance benchmarks, and risk analyses to justify their stance. They demonstrate data-driven restraint and strategic thinking. Red-flag answers (1) rely solely on personal preference or dismiss stakeholder input. This question evaluates balanced Openness and pragmatic judgment.
Give an example of mediating a heated code-review thread.
Strong (5) candidates recount active listening, clarifying technical concerns, proposing compromise solutions, and documenting outcomes for future reference. They show empathy and conflict-management skills. A weak (1) candidate either stays silent or escalates tensions. This question maps directly to Agreeableness and collaborative resilience.
When pairing with a junior dev who keeps making the same mistake, how do you handle it?
Top-tier (5) engineers outline a scaffolded coaching approach: diagnosing knowledge gaps, providing targeted examples, and gradually increasing autonomy. They cite improvements in the junior engineer’s performance. A poor (1) response shows frustration or avoidance. This probes both Agreeableness and situational Extraversion.
Tell me about the toughest architecture decision you presented to non-technical stakeholders.
A 5-rated answer details framing technical trade-offs in business terms, soliciting stakeholder input, and using diagrams to build consensus. They share measurable outcomes, such as cost savings or risk mitigation. A red-flag (1) answer retreats to jargon or leaves stakeholders uninformed. This question assesses Extraversion in communication.
Recall the most stressful production incident you owned. How did you keep the team focused?
Exceptional (5) candidates describe incident response protocols, call-tree activation, real-time status updates, and post-mortem frameworks. They quantify reduction in mean time to recovery. Candidates scoring a 1 panic or blame others. This item targets Emotional Stability and crisis leadership.
Building Your Assessment Workflow
Creating a reliable, scalable hiring process for software engineers requires weaving together technical, personality, and collaborative signals into a coherent pipeline. Begin with a clear Success Profile derived from your tech stack, delivery cadence, and team interfaces. Automated code screens filter for baseline competence, after which a Big Five work-style assessment—tools like SeeMyPersonality can generate role-specific interview kits—surfaces patterns in discipline, innovation appetite, and stress resilience.
Next, structured behavioral interviews dive into those patterns with calibrated questions and scoring rubrics. Pair these conversations with job-sample sessions—take-home repos or live debugging pairs—to observe real-time problem solving and code hygiene. Finally, synthesize findings in a documented selection matrix that weights technical skill, Conscientiousness, collaboration traits, and learning agility. Regularly audit your metrics—predictive validity, time-to-hire, turnover—to refine the workflow over time and ensure that each new engineer accelerates team velocity rather than dragging it down.
Step-by-Step Hiring Process
1. Define Success Profile & Job Analysis
Map out the technical stack, sprint cadence, and key collaboration touchpoints—code reviews, DevOps handoffs, stakeholder demos. Collect input from engineering leads, product managers, and QA to pinpoint must-have skills and critical personality traits. Document this profile to guide each assessment stage and ensure alignment across hiring stakeholders.
2. Conduct Initial Technical Screen
Use résumé filters and a brief automated coding challenge to verify baseline competency. Aim for a test that mimics common tasks—API integration or bug fixes—to boost face validity. Keep this stage concise (15–20 minutes) to quickly eliminate candidates unlikely to meet minimum skill requirements.
3. Administer Personality & Work-Style Assessment
Deploy a validated Big Five instrument, such as SeeMyPersonality or a comparable tool, immediately after the technical screen. Use forced-choice items and inconsistency flags to reduce response distortion. Analyze trait scores to shortlist candidates who exhibit the discipline, adaptability, and resilience your team needs.
4. Execute Structured Interview & Work Sample Loop
Schedule behavioral interviews with calibrated questions tied to each Big Five trait, followed by a job-sample pairing session or take-home code exercise. Score each response on a 1–5 rubric and log qualitative notes. This combined approach assesses both thought process and actual coding craft.
5. Apply Weighted Selection Matrix & Debrief
Convene the hiring panel to review scores in a transparent matrix—40% technical, 20% Conscientiousness, 15% collaboration (Agreeableness/Extraversion), 15% learning agility, and 10% reference checks. Discuss outliers and contextual factors before reaching consensus. Document decisions and feedback for continuous workflow improvement.
Key Hiring Metrics
Common Hiring Mistakes (and How to Avoid Them)
One frequent misstep is over-emphasizing live coding puzzles that lack real-world relevance. While entertaining, these whiteboard exercises often fail to predict on-the-job performance and unfairly penalize candidates unfamiliar with interview frameworks. A better alternative is job-sample work—such as a take-home repository or paired debugging session—combined with structured behavioral questions. This approach yields validity coefficients as high as r ≈ .54 and provides direct insight into daily collaboration dynamics.
Another pitfall is relying on culture-fit gut feel, which often masks unconscious bias and undermines team diversity. Instead, calibrate team-fit using objective Big Five or HEXACO scales linked to your core collaboration traits. Skipping reference probes on teamwork and communication is equally risky; standardized, behavior-based questions to former managers uncover patterns that résumé bullets cannot. Finally, avoid treating personality assessments as pass/fail gates—use them in a compensatory model where extreme scores trigger targeted coaching rather than automatic rejection.
Mistakes to Watch For
Overemphasizing Live Coding Puzzles
Live puzzles can feel relevant, but meta-analytic evidence shows they correlate poorly with real-world performance. Candidates often game these interviews through practice rather than genuine skill, leading to false positives. Replace them with take-home or pair-programming exercises that mirror everyday tasks. This shift boosts face validity and candidate experience simultaneously.
Relying on “Culture Fit” Gut Feel
Unstructured assessments of culture fit invite bias and often lead to homogenous teams. What feels like ‘fit’ can actually be affinity for similar backgrounds or communication styles. Instead, define objective team-fit criteria—collaboration cadence, feedback preferences, conflict norms—and measure against them. This guards against echo chambers and supports sustained innovation.
Treating Personality Tests as Pass/Fail Gates
Personality assessments should inform, not exclude. Labeling candidates as ‘unqualified’ based on a single trait oversimplifies complex behaviors and risks overlooking growth potential. Use assessments in a compensatory framework, where strong technical skill can offset moderate trait scores, and low scores trigger onboarding support. This nuanced approach maximizes workforce development.
Neglecting Collaboration Signals
Focusing solely on code output overlooks 84 percent of engineers’ time spent on meetings, maintenance, and reviews. Missing collaboration blind spots exacerbates technical debt and slows release cycles. Integrate behavioral questions and reference checks that probe teamwork, mentorship, and cross-team communication. Capturing these signals early prevents friction in live sprints.
After the Hire: Setting Up for Success
Onboarding does not end at code check-outs; it starts there. With candidate consent, share a high-level summary of personality insights with engineering managers and mentors, focusing on strengths like disciplined work habits or adaptive learning styles. Pair new hires with senior buddies whose traits complement theirs—high Openness mentors can guide stable, low-Openness coders through innovation challenges, while detail-oriented peers can support those needing stronger process discipline.
Establish clear feedback loops: set expectations around code-review etiquette, sprint participation, and communication channels. Schedule check-ins at the 30-, 60-, and 90-day marks to discuss progress against the Success Profile, adjust collaboration norms, and identify coaching needs. This structured, trait-informed approach accelerates integration, reduces early-stage churn, and ensures that personality insights translate into tangible performance gains.
Related Resources
Explore more on this topic
For Employers
Use personality assessments for hiring
Run the same research-backed assessments on job candidates. Get personality profiles, structured interview guides, and data-driven hiring insights.
Ready to Transform Your Hiring?
Use scientifically validated personality assessments to make better hiring decisions.