Interview guides · ERM

ERM Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at ERM, you play a vital role in building and supporting digital tools that drive environmental sustainability, compliance, and risk management solutions. This position sits at the intersection of modern software development and complex scientific data, enabling global clients to track, analyze, and mitigate environmental impacts. Your work directly impacts internal operations, field consultants, and external stakeholders who rely on robust, scalable applications to make critical business decisions. The work environment at ERM combines the scale of a global consultancy with the technical challenges of specialized engineering. You will collaborate with multidisciplinary teams—including environmental scientists, data analysts, and project managers—to translate complex regulatory and engineering requirements into intuitive software products.

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

Focus areas

Technical interviewing (problem solving)
Coding tests / coding assignments
Technical background / subject-matter knowledge

The ERM interview process

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

Application Review

Initial review of your application to assess qualifications and fit for the role.

What to demonstrate

Initial review of your application to assess qualifications and fit for the role
Depth in Technical interviewing (problem solving)

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.

Written Questionnaire

You may be asked to complete a written questionnaire to demonstrate foundational skills.

What to demonstrate

You may be asked to complete a written questionnaire to demonstrate foundational skills
Depth in Technical interviewing (problem solving)

How to prepare

Answer aloud and timed: What is your approach to writing clean, maintainable, and well-tested code in a collaborative team environment?
Answer aloud and timed: How would you design a data pipeline to ingest and validate large volumes of environmental or sensor data?

Coding Assessment

A coding assessment may be conducted to evaluate your technical abilities.

What to demonstrate

A coding assessment may be conducted to evaluate your technical abilities
Depth in Technical interviewing (problem solving)

How to prepare

Answer aloud and timed: Tell me about a time you had to deal with a difficult stakeholder or team member and how you resolved the conflict.
Answer aloud and timed: What do you consider your greatest professional weakness, and what steps are you actively taking to improve it?

Technical Take-Home Assignment

You might be given a technical take-home assignment to showcase your problem-solving skills.

What to demonstrate

You might be given a technical take-home assignment to showcase your problem-solving skills
Depth in Technical interviewing (problem solving)

How to prepare

Answer aloud and timed: Why do you want to work for ERM, and what interests you about software engineering in the sustainability sector?
Answer aloud and timed: Describe a situation where you had to learn a completely new technology or domain on a tight deadline.

Interviews with Team

Engagements with recruiters, technical leads, and senior engineers to assess technical and behavioral alignment.

What to demonstrate

Engagements with recruiters, technical leads, and senior engineers to assess technical and behavioral alignment
Depth in Technical interviewing (problem solving)

How to prepare

Answer aloud and timed: How would you estimate the carbon emissions or environmental footprint of a large manufacturing facility using proxy data?
Answer aloud and timed: Walk me through how you would troubleshoot a production system outage where the root cause is entirely unknown.

Final Decision

A final decision is made regarding your application based on the evaluations from previous steps.

What to demonstrate

A final decision is made regarding your application based on the evaluations from previous steps
Depth in Technical interviewing (problem solving)

How to prepare

Answer aloud and timed: If a client requests a feature that you know will introduce significant technical debt, how do you handle the conversation?
Work Technical interviewing (problem solving) until you can explain it without notes

Where candidates lose points

Showcase your written communication

Because pre-interview questionnaires and technical documentation writing are common touchpoints at ERM, ensure your written responses are polished, professional, and concise.

Demonstrate domain curiosity

Familiarize yourself broadly with environmental consulting, sustainability reporting, or ESG data concepts so you can speak intelligently about the problems ERM solves.

Talk through your trade-offs

During system design and architectural interviews, do not just give a single answer; explicitly discuss the pros and cons of your chosen approach regarding scalability and cost.

Be collaborative and approachable

Interviewers are evaluating whether they enjoy working with you day-to-day. Approach every interview as a collaborative discussion rather than an interrogation.

Questions & practice

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?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

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?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Can you explain a complex technical project from your past experience and walk through the architecture you de

Medium · Technical and Domain Knowledge

Can you explain a complex technical project from your past experience and walk through the architecture you designed?

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?

What is your approach to writing clean, maintainable, and well-tested code in a collaborative team environment

Medium · Technical and Domain Knowledge

What is your approach to writing clean, maintainable, and well-tested code in a collaborative team environment?

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 would you design a data pipeline to ingest and validate large volumes of environmental or sensor data?

Medium · Technical and 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 would you estimate the carbon emissions or environmental footprint of a large manufacturing facility using

Medium · Problem-Solving and Case Studies

How would you estimate the carbon emissions or environmental footprint of a large manufacturing facility using proxy data?

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 me through how you would troubleshoot a production system outage where the root cause is entirely unknown

Medium · Problem-Solving and Case Studies

Walk me through how you would troubleshoot a production system outage where the root cause is entirely unknown.

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 ERM candidates report.

Day 1 — Map the ERM loop

Write out the reported sequence: Application Review, Written Questionnaire, Coding Assessment, Technical Take-Home Assignment, Interviews with Team, 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 6 reported rounds, with the weakest marked.

Day 2 — Work Technical interviewing (problem solving)

Spend the session on Technical interviewing (problem solving), which ERM candidates report being tested on.
Write one worked example in Technical interviewing (problem solving) and time yourself on it.

Deliverable: One timed worked example in Technical interviewing (problem solving).

Day 3 — Work Coding tests / coding assignments

Spend the session on Coding tests / coding assignments, which ERM candidates report being tested on.
Write one worked example in Coding tests / coding assignments and time yourself on it.

Deliverable: One timed worked example in Coding tests / coding assignments.

Day 4 — Work Technical background / subject-matter knowledge

Spend the session on Technical background / subject-matter knowledge, which ERM candidates report being tested on.
Write one worked example in Technical background / subject-matter knowledge and time yourself on it.

Deliverable: One timed worked example in Technical background / subject-matter knowledge.

Day 5 — Answer out loud: Technical and Domain Knowledge

Answer aloud, timed: Can you explain a complex technical project from your past experience and walk through the architecture you designed?
Answer aloud, timed: How do you handle asynchronous data processing and ensure database performance under heavy loads?

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

Day 6 — Answer out loud: Behavioral and Cultural Fit

Answer aloud, timed: Tell me about a time you had to deal with a difficult stakeholder or team member and how you resolved the conflict.
Answer aloud, timed: What do you consider your greatest professional weakness, and what steps are you actively taking to improve it?

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

Day 7 — Answer out loud: Problem-Solving and Case Studies

Answer aloud, timed: How would you estimate the carbon emissions or environmental footprint of a large manufacturing facility using proxy data?
Answer aloud, timed: Walk me through how you would troubleshoot a production system outage where the root cause is entirely unknown.

Deliverable: Spoken answers to 2 reported Problem-Solving and Case Studies question(s), under time.

Behavioural preparation

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

How do you handle asynchronous data processing and ensure database performance under heavy loads?

Medium · Technical and Domain Knowledge

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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 me about a time you had to deal with a difficult stakeholder or team member and how you resolved the conf

Medium · Behavioral and Cultural Fit

Tell me about a time you had to deal with a difficult stakeholder or team member and how you resolved the conflict.

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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?

What do you consider your greatest professional weakness, and what steps are you actively taking to improve it

Medium · Behavioral and Cultural Fit

What do you consider your greatest professional weakness, and what steps are you actively taking to improve it?

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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?

Why do you want to work for ERM, and what interests you about software engineering in the sustainability secto

Medium · Behavioral and Cultural Fit

Why do you want to work for ERM, and what interests you about software engineering in the sustainability sector?

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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 situation where you had to learn a completely new technology or domain on a tight deadline.

Medium · Behavioral and Cultural Fit

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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?

If a client requests a feature that you know will introduce significant technical debt, how do you handle the

Medium · Problem-Solving and Case Studies

If a client requests a feature that you know will introduce significant technical debt, how do you handle the conversation?

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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?