Higher People Software Engineer Interview: Questions & Prep (2026)

Higher People is an HR tech and talent solutions company that builds products helping businesses hire, manage, and retain their workforce. As of July 2026, they have 42 open Software Engineer positions, which signals active engineering growth rather than occasional backfill hiring. Their engineering teams work on problems like candidate matching, resume parsing, and people analytics dashboards.

Candidates typically report a process covering three to four rounds: an initial screening with an HR contact, one or two technical rounds focused on coding and system design, and a final round with an engineering manager or a cross-functional panel. Rounds are typically conducted over video call. Because the product touches HR workflows directly, expect questions that blend standard computer science fundamentals with domain-adjacent thinking around data privacy and user experience for recruiters and HR managers.

The questions below are drawn from what candidates commonly report for product-focused engineering companies in the HR tech space. Prepare each one with a specific example or a thought-out design from your own work.

1. How would you design a job recommendation engine that matches candidates to relevant openings based on their resume and preferences?
2. Walk us through a time you optimised a slow database query or reduced API latency in a production system.
3. How would you build a scalable notification service to send emails and SMS to a large user base reliably and without message loss?
4. Describe a feature you owned end-to-end, from requirement gathering through to deployment and post-launch monitoring.
5. How do you handle a situation where product requirements and technical constraints are pulling in opposite directions?
6. How would you design a resume parsing service that extracts structured data from PDF and DOCX files accurately and at scale?
7. Tell us about a time you debugged a critical production issue under time pressure. What was your step-by-step process?
8. How do you ensure data privacy and security when your system handles sensitive candidate or employee records?
9. Describe your approach to designing REST APIs: how do you version them and prevent breaking changes for existing clients?
10. How would you build a real-time hiring pipeline dashboard showing metrics like time-to-hire and stage conversion rates?
11. Tell us about a time you disagreed with a teammate on a technical decision. How did you reach a resolution?
12. What does 'good code' mean to you, and how do you maintain that standard while shipping quickly in a product team?

Use the STAR format for every behavioural and experience-based question. Keep Situation and Task brief and spend most of your time on Action and Result. Here are three worked examples.

Q: Walk us through a time you optimised a slow query or reduced API latency.

*Situation:* At my previous company, a candidate-search API used by internal recruiters was taking well over five seconds to return results, making the tool nearly unusable during peak hiring periods.

*Task:* I was asked to investigate and bring response time down to under two seconds without a full rewrite of the service.

*Action:* I profiled the query using the database EXPLAIN plan and found it was doing a full table scan on a resume records table that lacked a composite index on the most commonly filtered columns. I added the index, rewrote a correlated subquery as a JOIN, and introduced a short-TTL cache for the most frequent filter combinations.

*Result:* Response time dropped to well under two seconds. Recruiter satisfaction with the tool improved noticeably the following sprint, and database load from that query dropped significantly.

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Q: Describe a feature you owned end-to-end.

*Situation:* My team needed a bulk-upload feature so HR managers could import employee records via CSV instead of adding them one by one through the UI.

*Task:* I was the sole engineer responsible for design, implementation, and rollout.

*Action:* I wrote a technical spec first and had it reviewed before writing any code. I built a background job queue so large files would not block the web process, added row-level validation with clear error messages returned to the user, and wrote integration tests covering malformed CSVs and duplicate records. I coordinated with the product manager for user acceptance testing and wrote the deployment runbook.

*Result:* The feature shipped on schedule. HR managers at three pilot clients adopted it within the first week, and support tickets related to manual data entry dropped noticeably in the month after launch.

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Q: Tell us about a time you disagreed with a teammate on a technical decision.

*Situation:* A senior engineer on my team wanted to split a module into separate microservices. I felt the module was not yet complex enough to justify the operational overhead.

*Task:* I needed to make my case clearly without creating conflict, since we both had to live with the outcome.

*Action:* I wrote a short comparison document laying out the added deployment complexity, inter-service latency, and risk of distributed transactions against the benefits of independent scaling. I proposed a middle path: a well-bounded module within the existing monolith that could be extracted later if traffic warranted it.

*Result:* The team adopted the bounded-module approach. Several months later the module had not yet needed extraction, which validated the decision. The senior engineer later said the written comparison was useful because it made trade-offs concrete rather than abstract.

STAR (for behavioural questions): Situation, Task, Action, Result. Keep Situation and Task to two or three sentences combined and spend most of your answer on Action and Result. Quantify results wherever you honestly can, even rough relative terms ('dropped by half', 'cut support tickets noticeably') are better than no signal at all.

Problem decomposition (for system design questions): Start by clarifying requirements and expected scale. Sketch the high-level components: clients, API layer, services, storage, queues. Drill into the component the interviewer seems most interested in. Discuss trade-offs at each step rather than presenting one answer as if it were the only correct one.

Think-aloud (for coding questions): Before writing code, restate the problem in your own words and explain your intended approach. This gives the interviewer a chance to redirect you early and shows you can communicate while problem-solving. After writing a working solution, trace through a test case by hand before optimising.

Domain framing (specific to HR tech roles): When answering design or product questions, anchor your thinking in the recruiter or HR manager's actual pain point. A comment like 'the recruiter is reviewing hundreds of profiles so this list must load in under a second' shows product sense and is valued at companies whose core product is people operations.

Based on what candidates typically report from HR tech engineering interviews, interviewers at product companies like Higher People look for a few specific signals beyond raw coding ability.

Product thinking: Engineers here build tools that real HR managers and recruiters use every day. Showing you understand the end user's workflow, not just the technical spec, consistently stands out in interviews.

Ownership mindset: Expect questions about work you drove independently. Interviewers want to see that you took responsibility from requirement to deployment, not just that you implemented a ticket someone else fully designed.

Clear communication: A company that sells people and communication tools pays attention to how clearly you explain a technical decision to a non-technical colleague. Avoid heavy jargon in behavioural and cross-functional rounds.

Data awareness: HR tech handles sensitive personal information. Candidates who proactively mention access controls, encryption at rest, and audit logging in their design answers score higher than those who only address privacy when directly prompted.

Pragmatism over perfectionism: Growth-stage companies value engineers who make a sound decision quickly and revisit it with new data. Interviewers notice when a candidate spends an entire design round debating architecture without arriving at a working plan.

Week 1: Fundamentals and coding practice
Review data structures and algorithms that come up most in product-company interviews: arrays, hash maps, trees, graphs, and dynamic programming basics. Solve at least one problem per day on a practice platform, focusing on problems commonly cited as 'medium' difficulty, as these are the most frequently reported for roles at this experience level.

Week 2: System design
Study two or three system design patterns directly relevant to HR tech: search and ranking systems (for job or candidate matching), notification pipelines, and file-processing queues (for resume or document ingestion). Practice explaining each design out loud, not just sketching it on paper, as verbal walkthroughs surface gaps that writing does not.

Week 3: Behavioural preparation
List six to eight strong examples from your work history covering: a hard technical problem you solved, a feature you owned end-to-end, a disagreement you navigated, a time you improved a process, and a failure you learned from. Write a two-sentence STAR outline for each so you can recall them clearly under interview pressure.

Final days: Company-specific research
Read whatever Higher People has published about their product, engineering culture, or recent team updates. Look at the job description for your specific role and identify the two or three skills they emphasise most. Prepare at least two genuine questions to ask your interviewer about the team's current technical challenges.

If you are actively applying while preparing, knok checks 150+ job sites nightly, applies to jobs that match your resume, and messages HR on your behalf, so you can focus your energy on interview prep rather than manual job searching.

Jumping to code without clarifying the problem. Interviewers consistently report that candidates who start coding immediately, without restating the problem or asking about edge cases, score lower even when their final code is correct. Take sixty seconds to align on the problem before writing anything.

Generic behavioural answers. Saying 'I am a team player' or 'I always write clean code' without a specific example is a red flag at every level. Every behavioural answer needs a real situation, a real action you took, and a concrete result you can describe.

Ignoring data privacy in design questions. In HR tech, every system you design handles personally identifiable information. Candidates who never mention access controls, encryption at rest, or audit logs leave interviewers with doubts about engineering maturity.

Over-engineering system design answers. Adding multiple caches, message queues, and distributed coordination to a problem that a single service and a scheduled job could handle signals poor judgement about cost and complexity. Match your design to the scale the interviewer actually gives you.

Not asking questions at the end. Candidates who say 'I have no questions' signal low interest. Prepare two genuine questions about the team, the product roadmap, or the biggest technical challenge the team is solving right now.

Poor time management in coding rounds. Spending the entire round chasing an optimal solution and never producing working code is a worse outcome than submitting a brute-force solution first and then improving it. Always aim for working code before refining.