Socotra Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Socotra, you are at the forefront of modernizing the insurance technology landscape. You are responsible for building the core infrastructure that enables insurance companies to launch products faster and scale more reliably. This role is not just about writing code; it is about architecting distributed systems that handle critical financial data and complex business logic. You will contribute to a platform that is revolutionizing a traditionally slow-moving industry. Whether you are working on the Risk Platform, integrating AI-driven insights, or optimizing the scalability of our claims management systems, your work directly impacts how insurance providers operate globally. You will be expected to balance high-level system design with rigorous attention to implementation details, ensuring that our platform remains robust, secure, and performant.

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

Focus areas

• System Design
• Distributed Systems
• Claims Management Systems (Domain Systems)

The Socotra interview process

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

Recruiter Screen

Initial contact with a recruiter to discuss your background and fit for the role.

What to demonstrate

• Initial contact with a recruiter to discuss your background and fit for the role
• Depth in System Design

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 Assessments

Engage in technical assessments including coding pairs and architectural discussions.

What to demonstrate

• Engage in technical assessments including coding pairs and architectural discussions
• Depth in System Design

How to prepare

• Answer aloud and timed: Implement efficient sorting algorithms.
• Answer aloud and timed: Discuss space and time complexity for your provided solutions.

Collaborative Interactions

Interact with multiple members of the engineering and management teams.

What to demonstrate

• Interact with multiple members of the engineering and management teams
• Depth in System Design

How to prepare

• Answer aloud and timed: Design a Claims Management System from the ground up.
• Answer aloud and timed: Explain your approach to testing and code review procedures.

Final Rounds

Participate in onsite or final rounds of interviews to evaluate overall fit.

What to demonstrate

• Participate in onsite or final rounds of interviews to evaluate overall fit
• Depth in System Design

How to prepare

• Answer aloud and timed: Discuss how to maintain system performance as transaction volume scales.
• Answer aloud and timed: Describe a time you had to resolve a technical disagreement within your team.

Where candidates lose points

Own your experience

When discussing past projects, be precise about what you did versus what the team did.

Think out loud

During technical screens, talk through your thought process. Interviewers are interested in how you approach problems, not just the final result.

Research the product

Spend time on the Socotra website. Understanding the insurance lifecycle will give you a significant edge in the system design rounds.

Ask thoughtful questions

At the end of your interviews, ask about the team’s current technical challenges or the company’s long-term roadmap.

Questions & practice

Calculate string values based on numeric inputs.

Medium · Coding and 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?

Find the next node in a Binary Tree.

Medium · Coding and 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?

Implement efficient sorting algorithms.

Medium · Coding and 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?

Discuss space and time complexity for your provided solutions.

Medium · Coding and 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 a Claims Management System from the ground up.

Medium · System Design and 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 how to maintain system performance as transaction volume scales.

Medium · System Design and 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?

Listing latency scales with page size, not with filters

Easy · n+1 · latency · query batching

The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.

Approach

1. Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
2. Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
3. Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
4. Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.

Follow-up

• The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
• How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?

Suggested practice checklist

Built from the rounds and topics Socotra candidates report.

Day 1 — Map the Socotra loop

• Write out the reported sequence: Recruiter Screen, Technical Assessments, Collaborative Interactions, Final Rounds.
• 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 System Design

• Spend the session on System Design, which Socotra 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 3 — Work Distributed Systems

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

Deliverable: One timed worked example in Distributed Systems.

Day 4 — Work Claims Management Systems (Domain Systems)

• Spend the session on Claims Management Systems (Domain Systems), which Socotra candidates report being tested on.
• Write one worked example in Claims Management Systems (Domain Systems) and time yourself on it.

Deliverable: One timed worked example in Claims Management Systems (Domain Systems).

Day 5 — Answer out loud: Coding and Algorithms

• Answer aloud, timed: Calculate string values based on numeric inputs.
• Answer aloud, timed: Find the next node in a Binary Tree.

Deliverable: Spoken answers to 2 reported Coding and Algorithms question(s), under time.

Day 6 — Answer out loud: System Design and Architecture

• Answer aloud, timed: Design a Claims Management System from the ground up.
• Answer aloud, timed: Explain your approach to testing and code review procedures.

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

Day 7 — Answer out loud: Behavioral and Cultural Fit

• Answer aloud, timed: Describe a time you had to resolve a technical disagreement within your team.
• Answer aloud, timed: How do you handle ambiguity when requirements are not fully defined?

Deliverable: Spoken answers to 2 reported Behavioral and Cultural Fit question(s), under time.

Behavioural preparation

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

Explain your approach to testing and code review procedures.

Medium · System Design and Architecture

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?

Describe a time you had to resolve a technical disagreement within your team.

Medium · Behavioral and Cultural Fit

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 ambiguity when requirements are not fully defined?

Medium · Behavioral and Cultural Fit

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?

Tell us about a complex project you led and the impact it had on the business.

Medium · Behavioral and Cultural Fit

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?