Full Stack Software Engineer

Ryan Mahoney

Why this role is hard · Ryan Mahoney

The hardest thing about hiring full stack engineers at this level is finding someone who can genuinely own a complete user journey without becoming a liability when complexity spikes. You need someone who writes solid code across the stack, sure, but more importantly, someone who can explain why they picked Postgres over DynamoDB for this specific workload, and walk a junior through that same thinking. The communication bar is higher than most teams want to admit. A candidate who can ship a feature end-to-end but cannot articulate trade-offs to product or push back on unrealistic deadlines will eventually become a bottleneck. You are hiring for judgment spread across layers, not just technical breadth.

Core Evaluation

Critical questions for this role

The competency and attitude questions below are where the hiring decision is made. They run in the live interview rounds and are calibrated to the level selected above.

27 Competency Questions

1 of 27

  1. Discipline

    Application Engineering & Architecture

  2. Job requirement

    Backend API & Service Development

    Designs RESTful/GraphQL APIs with proper authentication and authorization; implements middleware for logging, security, and request validation; manages database transactions and service integrations.

  3. Expected at Mid

    Mid-level engineers must independently design secure, scalable API contracts and manage service integrations to establish reliable backend functionality. This prevents security vulnerabilities, data inconsistencies, and brittle frontend integrations that cause cascading system failures.

Interview round: Hiring Manager System Design

Share an experience where you had to debug a production issue that originated in your backend service but manifested elsewhere.

Positive indicators

  • Describes distributed tracing or correlation ID usage
  • Mentions log aggregation and query strategies
  • Shows structured hypothesis formation and testing
  • Discussed post-incident improvements to observability or testing

Negative indicators

  • Blamed other services without investigating their own
  • No systematic approach, just tried things randomly
  • Fixed symptom without understanding root cause

15 Attitude Questions

1 of 15

Active Listening

The disciplined cognitive practice of fully concentrating on, comprehending, and retaining communicated information while suspending premature judgment, formulation of response, or solution generation. In technical contexts, this involves parsing both explicit content and implicit signals—detecting gaps between stated and intended meaning, recognizing emotional or political subtext, and verifying understanding through calibrated inquiry—particularly when bridging semantic divides between technical and non-technical stakeholders, or when extracting tacit knowledge that resists formal documentation.

Interview round: Hiring Manager System Design

You're in a squad planning session where a designer is presenting a complex interaction flow. Halfway through, you realize you're not tracking with their reasoning. What do you do?

Positive indicators

  • Proposes brief pause to align
  • Uses paraphrasing technique
  • Offers to continue async if time-constrained
  • Checks understanding with specific questions

Negative indicators

  • Nods along without understanding
  • Waits until implementation to discover gaps
  • Interrupts dismissively or defensively
  • Expects designer to notice their confusion

Supporting Evaluation

How candidates earn the selection conversation

The goal is to reduce effort for everyone by collecting more useful signal before adding more interviews. Lightweight application prompts and structured screens help the panel focus live time on the candidates most likely to succeed.

Stage 1 · Application

Filter at the door

Runs the moment a candidate hits Submit. Disqualifying answers end the application; everything else is captured for review.

Knock-out Questions

1 of 2

Application Screen: Knock-out

Have you personally designed, built, or maintained a production double-entry ledger, payment processing system, or automated financial reconciliation pipeline?

Yes
Qualifies
No
Auto-decline

Video-Response Questions

1 of 3

Application Screen: Video Response

Describe how you would explain a necessary reduction in system reliability to accommodate a high-priority feature launch to a product manager who has no technical background. What specific points do you cover, and how do you handle their pushback?

Candidate experience

REC
0:42 / 2:00
1Record
2Review
3Submit

Response time

2 min

Format

Recorded video

Stage 2 · Resume Screening

Read the resume against fixed criteria

Reviewers score every application that clears the door against the same criteria. Stronger reviews advance to live interviews; weaker ones are archived without further screening.

Resume Review Criteria

8 criteria
Independently designs, builds, and maintains complete user journeys, embedding production monitoring and debugging capabilities.
Designs and implements reliable data flows, schema migrations, and external service integrations with proper error handling and idempotency.
Builds production-ready AI features, including retrieval pipelines, vector storage, and real-time user interfaces for AI responses.
Authors technical proposals, defines API contracts, and collaborates with product and design teams to translate requirements into actionable engineering plans.

Does the cover letter or personal statement convey clear relevance and familiarity with the job?

Does the resume indicate required academic credentials, relevant certifications, or necessary training?

Is the resume complete, well-organized, and free from formatting, spelling, and grammar mistakes?

Does the resume show relevant prior work experience?

Stage 3 · During Interviews

Where the hire is decided

Interview rounds use the competency and attitude questions outlined above, then add tests, work simulations, and presentations that reveal deeper evidence about how the candidate thinks and works.

Coding Test

Live Interview · Coding Test

Without AI

Write a robust Express webhook handler that verifies signatures, checks for idempotency using a database, and safely processes payment events. Discuss your approach to concurrency, retries, and error recovery as you implement.

Build a payment webhook endpoint that handles duplicate deliveries gracefully. Implement signature verification, idempotency key tracking, and transactional database updates. Ensure the endpoint returns correct HTTP status codes for retries and failures.

With AI

Use an AI assistant to draft the idempotency check and signature verification logic. Critically audit the generated code for concurrency safety, security vulnerabilities, and transactional integrity. Refine and explain your modifications.

Leverage AI to scaffold the webhook validation and idempotency layer. Identify potential race conditions, missing transaction boundaries, or insecure crypto usage in the AI output. Produce a production-ready handler and justify your security and reliability choices.

Response time

20 min

Positive indicators

  • Atomic idempotency checks using database constraints or Redis
  • Secure signature verification with constant-time comparison
  • Clear separation of validation, processing, and response logic
  • Thoughtful retry handling and DLQ routing strategies
  • Detection of AI-suggested non-atomic idempotency patterns and correction with DB constraints
  • Validation of cryptographic libraries and timing-safe comparisons
  • Explicit transaction wrapping for multi-step payment processing
  • Clear documentation of AI-assisted vs manually verified sections

Negative indicators

  • Race conditions in idempotency checks leading to double-charging
  • Ignoring webhook signature verification
  • Blocking the event loop with synchronous database calls
  • Returning generic 500s instead of provider-specific retry codes
  • Accepting AI-generated race-prone idempotency checks
  • Missing transaction boundaries around payment state updates
  • Failing to verify AI-suggested crypto or hashing implementations
  • Inability to explain why specific AI patterns were rejected

Presentation Prompt

Prepare a short deck walking us through a past project where you owned a complete user journey or microservice from interface to data layer. Discuss your technical approach, how you handled trade-offs (e.g., legacy system deprecation, performance vs. delivery speed), and how you collaborated with cross-functional partners.

Format

deck-and-walkthrough · 20 min · ~2 hr prep

Audience

Engineering lead, product manager, and senior engineer

What to prepare

  • A 3-5 slide deck summarizing the project context, your architectural decisions, key trade-offs, and measurable outcomes.
  • Focus on your specific contributions, decision-making process, and how you navigated constraints.

Deliverables

  • A 20-minute presentation and Q&A walking through your slides and defending your technical choices.

Ground rules

  • Use only work you are permitted to share; anonymize sensitive data or proprietary architecture if under NDA.
  • Keep slides concise; the evaluation focuses on your verbal walkthrough, reasoning, and Q&A responses.
  • Do not build new diagrams or speculative architectures for this exercise.

Scoring anchors

Exceeds
Articulates complex distributed system trade-offs with clarity, demonstrates strong full-stack ownership, and shows mature reflection on outcomes, iteration paths, and debt management.
Meets
Provides a coherent walkthrough of the project, explains key decisions, handles trade-offs competently, and answers questions clearly.
Below
Lacks depth in technical reasoning, struggles to explain trade-offs, or cannot clearly articulate their role, impact, or collaboration approach.

Response time

20 min

Positive indicators

  • Clearly articulates the 'why' behind technical choices, not just the 'what'.
  • Surfaces constraints and explicitly discusses trade-offs (e.g., latency vs. consistency, build vs. buy, technical debt vs. velocity).
  • Demonstrates ownership of the full stack and explains cross-functional collaboration patterns with product and design.
  • Reflects honestly on what worked, what didn't, and how they would iterate or deprecate legacy components.

Negative indicators

  • Focuses only on implementation details without explaining architectural rationale or constraints.
  • Ignores trade-offs or presents decisions as purely technical without business or user context.
  • Cannot clearly delineate their individual contributions from broader team efforts.
  • Deflects feedback or struggles to explain how they would handle a hypothetical failure in the system.

Work Simulation Scenario

Scenario. You are leading the migration of our user authentication database to support enterprise SSO (SAML/OIDC) without disrupting existing users. You have a 40-minute planning session with the Staff Database Engineer to align on the zero-downtime migration strategy, backfill approach, and rollback triggers.

Problem to solve. Construct a phased migration plan that ensures data consistency, defines backfill mechanics, and establishes clear go/no-go and rollback criteria.

Format

discovery-interview · 40 min · ~2 hr prep

Success criteria

  • Outlines a dual-write or expand/contract migration pattern
  • Identifies data consistency risks and reconciliation strategy
  • Defines explicit rollback triggers and communication plan
  • Balances migration velocity with system stability

What to review beforehand

  • Prisma Migrate documentation
  • LaunchDarkly phased rollout patterns
  • Current authentication schema overview

Ground rules

  • Drive the conversation to uncover technical constraints
  • Focus on sequencing and risk mitigation, not syntax
  • Clarify assumptions about legacy data quality
  • You have 40 minutes to produce a shared migration blueprint

Roles in scenario

Staff Database Engineer (informed_partner, played by peer)

Motivation. Protect database stability and ensure the migration can be executed safely, with clear observability and reversible steps.

Constraints

  • Production database CPU must stay below 70% during backfill
  • Schema changes require a 4-hour maintenance window for index rebuilds
  • Legacy auth records have inconsistent email casing and null fields

Tensions to introduce

  • Product wants enterprise SSO live in 3 weeks
  • Backfill script will run at night, but failure recovery is manual
  • Frontend rollout depends on backend schema versioning

In-character guidance

  • Provide honest answers about database capacity and legacy data quirks
  • Share operational constraints when asked
  • Validate realistic migration sequencing and rollback planning
  • Stay focused on data integrity and operational safety

Do not

  • Do not volunteer migration strategies without being asked
  • Do not coach the candidate through schema design or backfill logic
  • Do not solve the sequencing problem or provide a turnkey plan
  • Do not withhold critical constraints that the candidate reasonably asks for

Scoring anchors

Exceeds
Designs a robust, auditable migration strategy with automated reconciliation, explicit failure boundaries, and seamless frontend/backend decoupling.
Meets
Identifies a standard zero-downtime pattern, acknowledges data consistency risks, and defines reasonable rollback criteria.
Below
Relies on unverified assumptions, proposes high-risk migration steps, or fails to establish clear rollback and monitoring thresholds.

Response time

40 min

Positive indicators

  • Proposes a proven zero-downtime pattern (expand/contract or dual-write)
  • Asks about legacy data inconsistencies and defines reconciliation steps
  • Establishes clear, measurable rollback triggers before execution
  • Articulates how feature flags decouple deployment from migration

Negative indicators

  • Assumes legacy data is clean without verification
  • Proposes a big-bang migration or ignores downtime constraints
  • Lacks a concrete rollback strategy or success metrics
  • Struggles to sequence frontend and backend rollout dependencies

Progression Framework

This table shows how competencies evolve across experience levels. Each cell shows competency at that level.

Application Engineering & Architecture

5 competencies

CompetencyJuniorMidSeniorPrincipal
Backend API & Service Development

Implements simple REST endpoints following existing schemas; handles basic request validation and error responses.

Designs RESTful/GraphQL APIs with proper authentication and authorization; implements middleware for logging, security, and request validation; manages database transactions and service integrations.

Architects microservices boundaries and API gateways; designs rate limiting and throttling strategies; leads API versioning strategies.

Defines service-oriented and event-driven architecture standards; establishes enterprise API governance and developer portal strategies.

Core Programming & Logic Implementation

Writes functional code following established style guides and patterns; debugs syntax and logical errors with mentorship; executes unit tests for assigned features.

Independently develops complex business logic and features; performs code refactoring for performance and clarity; conducts peer code reviews to ensure maintainability and adherence to standards.

Designs reusable libraries and internal frameworks; establishes coding standards and best practices; architects complex algorithmic solutions.

Defines organization-wide language strategies and paradigms; leads compiler optimization and runtime efficiency initiatives; influences industry standards.

Data Persistence & Schema Design

Writes basic CRUD queries; follows existing schema conventions; uses ORM for simple data retrieval.

Designs normalized relational schemas that balance integrity and performance; implements backward-compatible database migrations; optimizes query performance through strategic indexing and query refactoring.

Architects data sharding and replication strategies; implements event sourcing and CQRS patterns; designs multi-model data solutions.

Sets organization-wide data storage strategy; evaluates distributed databases (CockroachDB, Spanner); drives data consistency models.

Distributed Systems & Communication Patterns

Consumes existing message queue endpoints; handles basic asynchronous operations; understands idempotency concepts.

Implements pub/sub patterns and background job processors for asynchronous workflows; manages circuit breakers and retry logic for resilience; handles distributed caching strategies.

Designs event-driven architectures and saga patterns; implements distributed tracing; manages consistency in distributed transactions.

Establishes distributed systems standards and patterns; researches consensus algorithms; designs multi-region active-active systems.

Frontend Engineering & UI Development

Implements UI components from design specifications; fixes styling and layout issues; learns framework-specific state management basics.

Builds complex interactive features with state management; optimizes bundle size and rendering performance to meet performance budgets; implements responsive and accessible designs adhering to WCAG standards.

Architects frontend state management and component libraries; establishes accessibility (WCAG) and security (CSP) standards; leads frontend migrations.

Sets frontend technology strategy across web and mobile platforms; drives adoption of micro-frontends and edge computing; evaluates emerging frameworks.

Platform Operations & Specialized Systems

6 competencies

CompetencyJuniorMidSeniorPrincipal
AI/ML System Integration

Integrates pre-trained model APIs into applications; handles basic input preprocessing and output formatting.

Deploys custom models to production environments; implements A/B testing frameworks for model variants; manages feature engineering pipelines and prompt engineering workflows.

Architects scalable model serving infrastructure; optimizes inference latency and cost; implements ML monitoring for drift and bias.

Establishes enterprise AI platforms and governance; drives MLOps maturity; ensures ethical AI practices and model version control.

Cloud Infrastructure & Automation

Deploys to pre-configured cloud environments; modifies existing Terraform/CloudFormation templates; monitors basic resource metrics.

Builds CI/CD pipelines with automated testing and deployment gates; manages container orchestration (Kubernetes) and auto-scaling policies; implements infrastructure monitoring and environment parity.

Designs multi-region, highly available infrastructure; implements disaster recovery and backup strategies; optimizes cloud costs and security posture.

Establishes cloud governance frameworks and FinOps practices; drives platform engineering initiatives; evaluates serverless and edge computing strategies.

Data Pipeline Engineering

Executes existing batch jobs; monitors pipeline failures; performs basic data validation checks.

Develops new data transformations and schema evolution handling for analytics; implements incremental loading patterns; optimizes pipeline performance for cost and latency.

Architects streaming data platforms and real-time analytics; ensures data quality, lineage, and GDPR compliance; designs lakehouse architectures.

Establishes data platform strategy and governance; drives data mesh implementations; evaluates novel storage formats (Iceberg/Delta Lake).

Financial Systems Engineering

Implements basic transaction recording and retrieval; ensures idempotency in payment operations; handles currency formatting.

Designs double-entry ledger systems for money movements; implements reconciliation and audit trail mechanisms; manages PCI-DSS compliance requirements for payment processing.

Architects high-volume payment processing systems; designs eventual consistency models for financial data; ensures SOX compliance.

Defines financial architecture standards and ledger strategies; drives real-time payment innovations; establishes fintech partnership integrations.

Observability & System Reliability

Monitors dashboards and escalates alerts; follows runbooks for common incidents; documents system status.

Configures monitoring agents, metrics dashboards, and log aggregation systems; participates actively in on-call rotations and incident response; performs root cause analysis to prevent recurrence.

Designs SLO/SLI frameworks and error budgets; leads blameless post-mortems; implements chaos engineering and automated remediation.

Establishes organizational reliability standards and SRE culture; architects global observability platforms; drives zero-downtime migration strategies.

Technical Leadership & Communication

Documents code and APIs clearly; participates in agile ceremonies; seeks feedback on implementation approaches.

Mentors junior developers through pair programming and constructive code reviews; leads small technical initiatives and spikes; writes Request for Comments (RFCs) and architecture design docs.

Leads cross-functional projects and architecture reviews; drives ADRs and technical roadmaps; manages stakeholder expectations.

Influences executive technical strategy and budget allocation; builds external engineering brand through speaking/writing; establishes mentorship programs.