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The Grw Group Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at The Grw Group operates at the vital intersection of physical engineering and digital innovation. Unlike traditional software companies that build isolated consumer web applications, The Grw Group focuses on complex, multi-disciplinary engineering, infrastructure, and industrial solutions. In this role, you will design, build, and maintain the software tools, data pipelines, and automation systems that empower civil, structural, mechanical, and water resource engineers to model and execute massive physical projects. Your work has a tangible impact on the real world. Whether you are optimizing GIS data processing systems, automating complex structural analysis workflows, or building custom integration tools for manufacturing and logistics, your code directly influences how infrastructure is designed and built.

This guide is scoped to a Software Engineer candidate at The Grw Group.

Focus areas

Software Engineering (Role/Expectations)
Water Resources Engineering
Water/Wastewater Engineering

The The Grw Group interview process

The Grw Group candidates report 2 rounds over 2-4 weeks. The stages below are what candidates describe, not a published process.

Initial Conversation

A direct conversation with a hiring manager and a senior staff member to discuss expectations and role realities.

What to demonstrate

A direct conversation with a hiring manager and a senior staff member to discuss expectations and role realities
Depth in Software Engineering (Role/Expectations)

How to prepare

Answer aloud and timed: How do you design a database schema to efficiently store and query highly structured spatial or geospatial data?
Answer aloud and timed: Describe a time you had to write a script or tool to automate a complex, manual workflow. What API or libraries did you use, and how did you handle errors?

Technical Evaluation

Focus on practical problem-solving and integration of software systems with engineering workflows.

What to demonstrate

Focus on practical problem-solving and integration of software systems with engineering workflows
Depth in Software Engineering (Role/Expectations)

How to prepare

Answer aloud and timed: How do you ensure data integrity when transferring large datasets between legacy engineering software and modern cloud databases?
Answer aloud and timed: What strategies do you use to optimize slow-running database queries or data processing scripts?

Where candidates lose points

Highlight integration experience

Whenever you talk about your past projects, emphasize times when you had to connect disparate systems, work with legacy codebases, or integrate software with physical processes.

Be clear about expectations

As noted in real interview experiences, both you and the hiring team should be highly transparent about what you are looking for. If you prefer building consumer-facing web apps over internal engineering tools, state that early.

Showcase your communication skills

Use the STAR method (Situation, Task, Action, Result) for behavioral questions, and explicitly highlight how you communicated technical software decisions to non-technical team members.

Questions & practice

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

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?

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?

How do you design a database schema to efficiently store and query highly structured spatial or geospatial dat

Medium · Technical & System Integration

How do you design a database schema to efficiently store and query highly structured spatial or geospatial data?

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

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

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.
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.
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.
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?

How do you ensure data integrity when transferring large datasets between legacy engineering software and mode

Medium · Technical & System Integration

How do you ensure data integrity when transferring large datasets between legacy engineering software and modern cloud databases?

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 strategies do you use to optimize slow-running database queries or data processing scripts?

Medium · Technical & System Integration

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?

Walk us through your process for designing a custom tool that must interface with a proprietary, third-party e

Medium · Problem-Solving & Architecture

Walk us through your process for designing a custom tool that must interface with a proprietary, third-party engineering API.

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?

How do you balance the trade-off between building a highly customized, bespoke software solution versus utiliz

Medium · Problem-Solving & Architecture

How do you balance the trade-off between building a highly customized, bespoke software solution versus utilizing off-the-shelf platforms?

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?

Describe how you would architect a scalable data pipeline that ingests continuous environmental or structural

Medium · Problem-Solving & Architecture

Describe how you would architect a scalable data pipeline that ingests continuous environmental or structural monitoring data.

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 approach refactoring a legacy codebase that lacks documentation but remains critical to daily engin

Medium · Problem-Solving & Architecture

How do you approach refactoring a legacy codebase that lacks documentation but remains critical to daily engineering operations?

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?

Exports duplicate a row range about once a week

Hard · leases · clock skew · idempotency

Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.

Approach

Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.

Follow-up

The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
You now count takeovers. What alert fires on that counter, and at what threshold?

Suggested practice checklist

Built from the rounds and topics The Grw Group candidates report.

Day 1 — Map the The Grw Group loop

Write out the reported sequence: Initial Conversation, Technical Evaluation.
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 2 reported rounds, with the weakest marked.

Day 2 — Work Software Engineering (Role/Expectations)

Spend the session on Software Engineering (Role/Expectations), which The Grw Group candidates report being tested on.
Write one worked example in Software Engineering (Role/Expectations) and time yourself on it.

Deliverable: One timed worked example in Software Engineering (Role/Expectations).

Day 3 — Work Water Resources Engineering

Spend the session on Water Resources Engineering, which The Grw Group candidates report being tested on.
Write one worked example in Water Resources Engineering and time yourself on it.

Deliverable: One timed worked example in Water Resources Engineering.

Day 4 — Work Water/Wastewater Engineering

Spend the session on Water/Wastewater Engineering, which The Grw Group candidates report being tested on.
Write one worked example in Water/Wastewater Engineering and time yourself on it.

Deliverable: One timed worked example in Water/Wastewater Engineering.

Day 5 — Answer out loud: Technical & System Integration

Answer aloud, timed: How do you design a database schema to efficiently store and query highly structured spatial or geospatial data?
Answer aloud, timed: Describe a time you had to write a script or tool to automate a complex, manual workflow. What API or libraries did you use, and how did you handle errors?

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

Day 6 — Answer out loud: Problem-Solving & Architecture

Answer aloud, timed: Walk us through your process for designing a custom tool that must interface with a proprietary, third-party engineering API.
Answer aloud, timed: How do you balance the trade-off between building a highly customized, bespoke software solution versus utilizing off-the-shelf platforms?

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

Day 7 — Answer out loud: Behavioral & Collaboration

Answer aloud, timed: Describe a situation where you had to explain a complex software concept to a non-technical stakeholder, such as a civil or mechanical engineer. How did you adapt your communication?
Answer aloud, timed: Tell us about a time you realized a project's requirements were unrealistic. How did you manage expectations with the project manager?

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

Behavioural preparation

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

Describe a time you had to write a script or tool to automate a complex, manual workflow. What API or librarie

Medium · Technical & System Integration

Describe a time you had to write a script or tool to automate a complex, manual workflow. What API or libraries did you use, and how did you handle errors?

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 explain a complex software concept to a non-technical stakeholder, such

Medium · Behavioral & Collaboration

Describe a situation where you had to explain a complex software concept to a non-technical stakeholder, such as a civil or mechanical engineer. How did you adapt your communication?

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 us about a time you realized a project's requirements were unrealistic. How did you manage expectations w

Medium · Behavioral & Collaboration

Tell us about a time you realized a project's requirements were unrealistic. How did you manage expectations with the project manager?

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?

Walk us through a time when you and another engineer disagreed on a technical approach. How did you resolve th

Medium · Behavioral & Collaboration

Walk us through a time when you and another engineer disagreed on a technical approach. How did you resolve the conflict and move the project forward?

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?

How do you handle a situation where a legacy system you are integrating with fails unexpectedly during a criti

Medium · Behavioral & Collaboration

How do you handle a situation where a legacy system you are integrating with fails unexpectedly during a critical project phase?

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?