Interview guides · Johnson

Johnson Software Engineer Interview Guide

Johnson Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

At Johnson & Johnson, a Software Engineer sits at the critical intersection of advanced technology and life-saving healthcare innovation. You are not just writing code; you are building the digital infrastructure that supports medical device development, supply chain optimization, and research-driven solutions. Your work directly impacts the company’s ability to deliver high-quality patient care, necessitating a high degree of technical precision and regulatory awareness. This role is both complex and deeply rewarding. You will likely engage with cross-functional teams, ranging from research scientists to clinical experts, to translate abstract requirements into scalable, compliant software solutions.

This guide is scoped to a Software Engineer candidate at Johnson.

Focus areas

Spring Boot
System Design
DSA (Data Structures & Algorithms)

The Johnson interview process

Johnson candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

Verification of basic qualifications and interest, often via a recruiter or an automated platform like HireVue.

What to demonstrate

Verification of basic qualifications and interest, often via a recruiter or an automated platform like HireVue
Depth in Spring Boot

How to prepare

Be able to walk your CV end to end in two minutes, and say why this company specifically.
Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Virtual/In-Person Interviews

Series of interviews with hiring managers and cross-functional team members to evaluate technical skills and cultural fit.

What to demonstrate

Series of interviews with hiring managers and cross-functional team members to evaluate technical skills and cultural fit
Depth in Spring Boot

How to prepare

Answer aloud and timed: What is your experience with microservices architecture and managing transactions in distributed systems?
Answer aloud and timed: How do you ensure data privacy and security when building cloud-based applications?

Technical Presentations/Case Studies

Potential multi-stage panels that may include technical presentations or case studies.

What to demonstrate

Potential multi-stage panels that may include technical presentations or case studies
Depth in Spring Boot

How to prepare

Answer aloud and timed: Can you walk us through your process for debugging a complex system issue?
Answer aloud and timed: Why do you want to work for Johnson & Johnson specifically?

Final Panel Interviews

Concluding interviews that assess overall fit and readiness for the role.

What to demonstrate

Concluding interviews that assess overall fit and readiness for the role
Depth in Spring Boot

How to prepare

Answer aloud and timed: Describe a time you had to handle a conflict within your team. How did you resolve it?
Answer aloud and timed: Can you share an example of a time you had to make an ethical decision at work?

Where candidates lose points

Review Your Projects

Be prepared to present a deep dive into a past project. You should be able to explain the architecture, the challenges, and the specific impact you made.

Ask Strategic Questions

Use the end of your interview to ask about the team’s current technical hurdles or how they balance innovation with compliance.

Master the STAR Method

Keep your answers concise and structured, focusing on the "Result" to show that you are an outcome-oriented engineer.

Be Transparent

If you don't know an answer, communicate your thought process. Interviewers often value how you approach a problem more than the final answer itself.

Questions & practice

Can you explain the difference between processes and threads, or multitasking versus multithreading?

Medium · Technical & Domain Knowledge

Approach

Say what the runtime actually does before reasoning about the code.
Name what is shared across threads and what owns each piece of state.
Identify the window where an invariant is briefly untrue.
Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

What happens if two callers reach this at the same time?
Where could this allocate more than you expect?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
A dashboard runs this for 40 owners in one page load. What changes about the design?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

How do you approach systems integration and defect tracking in a regulated environment?

Medium · Technical & Domain Knowledge

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
Design the error taxonomy before the success shape; callers branch on it.

Follow-up

What happens if the caller retries after a timeout?
How does a client discover it is on an old version of this contract?

What is your experience with microservices architecture and managing transactions in distributed systems?

Medium · Technical & Domain Knowledge

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How do you ensure data privacy and security when building cloud-based applications?

Medium · Technical & Domain Knowledge

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Walk us through a past project—what were the primary technical challenges, and how did you solve them?

Medium · Problem-Solving & Methodology

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

How do you prioritize tasks when faced with competing technical requirements?

Medium · Problem-Solving & Methodology

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

If you were given a legacy system to modernize, where would you start?

Medium · Problem-Solving & Methodology

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Describe your approach to testing and quality assurance—how do you ensure your code is production-ready?

Medium · Problem-Solving & Methodology

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Can you walk us through your process for debugging a complex system issue?

Medium · Technical & Domain Knowledge

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

What would you look at first, and what would it rule out?
How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics Johnson candidates report.

Day 1 — Map the Johnson loop

Write out the reported sequence: Initial Screening, Virtual/In-Person Interviews, Technical Presentations/Case Studies, Final Panel Interviews.
For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work Spring Boot

Spend the session on Spring Boot, which Johnson candidates report being tested on.
Write one worked example in Spring Boot and time yourself on it.

Deliverable: One timed worked example in Spring Boot.

Day 3 — Work System Design

Spend the session on System Design, which Johnson candidates report being tested on.
Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 4 — Work DSA (Data Structures & Algorithms)

Spend the session on DSA (Data Structures & Algorithms), which Johnson candidates report being tested on.
Write one worked example in DSA (Data Structures & Algorithms) and time yourself on it.

Deliverable: One timed worked example in DSA (Data Structures & Algorithms).

Day 5 — Answer out loud: Technical & Domain Knowledge

Answer aloud, timed: How do you approach systems integration and defect tracking in a regulated environment?
Answer aloud, timed: Can you explain the difference between processes and threads, or multitasking versus multithreading?

Deliverable: Spoken answers to 2 reported Technical & Domain Knowledge question(s), under time.

Day 6 — Answer out loud: Behavioral & Values-Based

Answer aloud, timed: Why do you want to work for Johnson & Johnson specifically?
Answer aloud, timed: Describe a time you had to handle a conflict within your team. How did you resolve it?

Deliverable: Spoken answers to 2 reported Behavioral & Values-Based question(s), under time.

Day 7 — Answer out loud: Problem-Solving & Methodology

Answer aloud, timed: Walk us through a past project—what were the primary technical challenges, and how did you solve them?
Answer aloud, timed: How do you prioritize tasks when faced with competing technical requirements?

Deliverable: Spoken answers to 2 reported Problem-Solving & Methodology question(s), under time.