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E2E Networks Software Engineer Interview Guide

E2E Networks Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at E2E Networks is at the forefront of cloud infrastructure and accelerated computing. In this role, you are not just writing code; you are building the systems that power high-performance environments, enabling businesses to scale their AI and machine learning workloads efficiently. Your work directly impacts the reliability and performance of cloud services that support a wide range of modern applications. The environment at E2E Networks is characterized by a focus on technical depth and practical problem-solving. You will contribute to projects involving cloud architecture, system-level optimizations, and potentially cutting-edge areas like LLMs and automated infrastructure. It is a role for engineers who thrive on understanding the "how" and "why" behind the stack, moving beyond high-level abstractions to ensure systems operate at peak capacity.

This guide is scoped to a Software Engineer candidate at E2E Networks.

Focus areas

  • DSA (Data Structures and Algorithms)
  • Algorithmic Problem Solving
  • Chatbot Systems

The E2E Networks interview process

E2E Networks candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

The process begins with initial screenings to assess candidate fit.

What to demonstrate

  • The process begins with initial screenings to assess candidate fit
  • Depth in DSA (Data Structures and Algorithms)

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.

Technical Rounds

Candidates undergo technical rounds covering theoretical knowledge and practical application.

What to demonstrate

  • Candidates undergo technical rounds covering theoretical knowledge and practical application
  • Depth in DSA (Data Structures and Algorithms)

How to prepare

  • Answer aloud and timed: Add ropes of minimum lengths given an array of lengths.
  • Answer aloud and timed: Trapping Rain Water problem.

Project Experience Review

Candidates are challenged on their resume and specific project experiences.

What to demonstrate

  • Candidates are challenged on their resume and specific project experiences
  • Depth in DSA (Data Structures and Algorithms)

How to prepare

  • Answer aloud and timed: Array-based manipulation questions.
  • Answer aloud and timed: Design an architecture for a chatbot system.

Leadership Review

Final stages include leadership or managerial reviews focusing on cultural alignment.

What to demonstrate

  • Final stages include leadership or managerial reviews focusing on cultural alignment
  • Depth in DSA (Data Structures and Algorithms)

How to prepare

  • Answer aloud and timed: Discuss current trends in LLMs and Rule-Based chatbots.
  • Answer aloud and timed: Explain your approach to system design trade-offs in a design assignment.

Final Decision

The process concludes with a final decision-making stage regarding the candidate's fit.

What to demonstrate

  • The process concludes with a final decision-making stage regarding the candidate's fit
  • Depth in DSA (Data Structures and Algorithms)

How to prepare

  • Answer aloud and timed: What are the benefits of using React?
  • Answer aloud and timed: Explain core concepts in Linux, Operating Systems (OS), and Computer Networks (CN).

Where candidates lose points

Own your resume

Every project listed is fair game. Be prepared to explain the architecture, the challenges, and the specific technical decisions you made.

Think out loud

During coding rounds, narrate your approach. This helps the interviewer understand your logic and provides them with opportunities to guide you.

Master the basics

Do not overlook OS and Networking fundamentals. These are frequently tested and are essential for a role in cloud infrastructure.

Practice writing

For some roles, you may be asked to demonstrate communication through writing or documentation. Ensure your technical writing is clear and concise.

Questions & practice

Implementation of LRU cache with code.

Medium · Data Structures & Algorithms

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Remove duplicates from a linked list.

Medium · Data Structures & Algorithms

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Add ropes of minimum lengths given an array of lengths.

Medium · Data Structures & Algorithms

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Trapping Rain Water problem.

Medium · Data Structures & Algorithms

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Array-based manipulation questions.

Medium · Data Structures & Algorithms

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

  1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
  2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
  3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
  4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

  • A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
  • How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

  1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
  2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
  3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
  4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

  • The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
  • How do you verify the backfill actually finished, given rows are still being inserted while it runs?

Design an architecture for a chatbot system.

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

Discuss current trends in LLMs and Rule-Based chatbots.

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

Explain your approach to system design trade-offs in a design assignment.

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

What are the benefits of using React?

Medium · Computer Fundamentals & Technical Depth

Approach

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

Follow-up

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

Explain core concepts in Linux, Operating Systems (OS), and Computer Networks (CN).

Medium · Computer Fundamentals & Technical Depth

Approach

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

Follow-up

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

Questions regarding OOPS concepts and JavaScript.

Medium · Computer Fundamentals & Technical Depth

Approach

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

Follow-up

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

Specific language proficiency questions (e.g., Python and Django).

Medium · Computer Fundamentals & Technical Depth

Approach

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

Follow-up

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

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

  1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
  2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
  3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
  4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

  • Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
  • How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics E2E Networks candidates report.

Day 1 — Map the E2E Networks loop

  • Write out the reported sequence: Initial Screening, Technical Rounds, Project Experience Review, Leadership Review, Final Decision.
  • 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 5 reported rounds, with the weakest marked.

Day 2 — Work DSA (Data Structures and Algorithms)

  • Spend the session on DSA (Data Structures and Algorithms), which E2E Networks candidates report being tested on.
  • Write one worked example in DSA (Data Structures and Algorithms) and time yourself on it.

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

Day 3 — Work Algorithmic Problem Solving

  • Spend the session on Algorithmic Problem Solving, which E2E Networks candidates report being tested on.
  • Write one worked example in Algorithmic Problem Solving and time yourself on it.

Deliverable: One timed worked example in Algorithmic Problem Solving.

Day 4 — Work Chatbot Systems

  • Spend the session on Chatbot Systems, which E2E Networks candidates report being tested on.
  • Write one worked example in Chatbot Systems and time yourself on it.

Deliverable: One timed worked example in Chatbot Systems.

Day 5 — Answer out loud: Data Structures & Algorithms

  • Answer aloud, timed: Implementation of LRU cache with code.
  • Answer aloud, timed: Remove duplicates from a linked list.

Deliverable: Spoken answers to 2 reported Data Structures & Algorithms question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

  • Answer aloud, timed: Design an architecture for a chatbot system.
  • Answer aloud, timed: Discuss current trends in LLMs and Rule-Based chatbots.

Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.

Day 7 — Answer out loud: Computer Fundamentals & Technical Depth

  • Answer aloud, timed: What are the benefits of using React?
  • Answer aloud, timed: Explain core concepts in Linux, Operating Systems (OS), and Computer Networks (CN).

Deliverable: Spoken answers to 2 reported Computer Fundamentals & Technical Depth question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Walk through your previous experience and future goals.

Medium · Behavioral & Communication

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Explain your design process and decision-making rationale.

Medium · Behavioral & Communication

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

How do you handle collaborative tasks or written communication requirements?

Medium · Behavioral & Communication

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?