Goliath Partners Software Engineer Interview Guide

The 60-second brief

At Goliath Partners, a Software Engineer is not merely a coder; you are a core contributor to the firm's competitive edge in the fast-paced world of systematic trading and market making. You will operate in a lean, research-driven environment where your code directly influences pricing, execution, and risk management. Your work bridges the gap between raw data and actionable market signals, making your technical output a direct driver of the firm’s profitability. This role is designed for engineers who thrive on end-to-end ownership. You will move beyond narrow development lanes, collaborating closely with quantitative researchers and traders to deploy high-performance systems. Whether you are building low-latency trading infrastructure, optimizing data pipelines, or integrating machine learning models, your contributions will be felt in real-time.

This guide is scoped to a Software Engineer candidate at Goliath Partners.

Focus areas

• Python
• Software Engineering (General)
• C++

The Goliath Partners interview process

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

Initial Screening

The first step involves a review of your application to assess fit for the role.

What to demonstrate

• The first step involves a review of your application to assess fit for the role
• Depth in Python

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 Deep-Dive

A series of technical interviews focusing on your problem-solving skills in real-world scenarios.

What to demonstrate

• A series of technical interviews focusing on your problem-solving skills in real-world scenarios
• Depth in Python

How to prepare

• Answer aloud and timed: What are the common bottlenecks encountered when scaling a distributed backtesting system?
• Answer aloud and timed: How do you ensure data integrity in a high-throughput pipeline?

Hands-On Coding

You will be required to write code in a live environment, emphasizing clean and efficient solutions.

What to demonstrate

• You will be required to write code in a live environment, emphasizing clean and efficient solutions
• Depth in Python

How to prepare

• Answer aloud and timed: Describe your approach to debugging a performance regression in a production trading system.
• Answer aloud and timed: How would you handle feature engineering for a model that needs to run in real-time?

System Design Review

Discussion and evaluation of high-level system design, particularly for senior roles.

What to demonstrate

• Discussion and evaluation of high-level system design, particularly for senior roles
• Depth in Python

How to prepare

• Answer aloud and timed: Explain the challenges of deploying a model that relies on streaming data versus batch data.
• Answer aloud and timed: How do you validate the performance of a predictive model before it reaches the trading engine?

Final Assessment

A concluding evaluation that may involve cross-team leadership discussions.

What to demonstrate

• A concluding evaluation that may involve cross-team leadership discussions
• Depth in Python

How to prepare

• Answer aloud and timed: Describe a time you had to optimize a machine learning pipeline to reduce latency.
• Answer aloud and timed: What statistical pitfalls should an engineer watch out for when working with market microstructure data?

Where candidates lose points

Focus on the "Why

Whenever you propose a solution, be ready to explain the trade-offs. We value engineers who understand the cost of their design choices.

Master the Basics

Don't overlook fundamentals like memory management and concurrency. These are the building blocks of the high-performance systems we build.

Own your answers

If you don't know an answer, be honest about it. We appreciate candidates who can reason through a problem they haven't seen before.

Do not focus solely on theoretical algorithms

Our interviews are highly practical and focused on the systems we actually build.

Questions & practice

How do you optimize memory management in a high-frequency trading environment?

Medium · Technical & Domain Expertise

Approach

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

Follow-up

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

Can you explain the trade-offs between different concurrency models when handling real-time market data?

Medium · Technical & Domain Expertise

Approach

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

Follow-up

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

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

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

Approach

1. 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.
2. 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.
3. 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.
4. 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?

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

1. 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.
2. 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.
3. 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.
4. 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?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

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

Approach

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

Follow-up

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

What are the common bottlenecks encountered when scaling a distributed backtesting system?

Medium · Technical & Domain Expertise

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?

How do you ensure data integrity in a high-throughput pipeline?

Medium · Technical & Domain Expertise

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?

How would you handle feature engineering for a model that needs to run in real-time?

Medium · Machine Learning & Statistical Modeling

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 the challenges of deploying a model that relies on streaming data versus batch data.

Medium · Machine Learning & Statistical Modeling

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?

How do you validate the performance of a predictive model before it reaches the trading engine?

Medium · Machine Learning & Statistical Modeling

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?

What statistical pitfalls should an engineer watch out for when working with market microstructure data?

Medium · Machine Learning & Statistical Modeling

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?

What is your process for integrating feedback from a quantitative researcher into your codebase?

Medium · Behavioral & Collaboration

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. 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?

Describe your approach to debugging a performance regression in a production trading system.

Medium · Technical & Domain Expertise

Approach

1. Establish what changed and when, before forming any theory.
2. Pick a bisection that eliminates candidates whichever way it turns out.
3. Check the instrumentation before believing the symptom.
4. 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 Goliath Partners candidates report.

Day 1 — Map the Goliath Partners loop

• Write out the reported sequence: Initial Screening, Technical Deep-Dive, Hands-On Coding, System Design Review, Final Assessment.
• 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 Python

• Spend the session on Python, which Goliath Partners candidates report being tested on.
• Write one worked example in Python and time yourself on it.

Deliverable: One timed worked example in Python.

Day 3 — Work Software Engineering (General)

• Spend the session on Software Engineering (General), which Goliath Partners candidates report being tested on.
• Write one worked example in Software Engineering (General) and time yourself on it.

Deliverable: One timed worked example in Software Engineering (General).

Day 4 — Work C++

• Spend the session on C++, which Goliath Partners candidates report being tested on.
• Write one worked example in C++ and time yourself on it.

Deliverable: One timed worked example in C++.

Day 5 — Answer out loud: Technical & Domain Expertise

• Answer aloud, timed: How do you optimize memory management in a high-frequency trading environment?
• Answer aloud, timed: Can you explain the trade-offs between different concurrency models when handling real-time market data?

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

Day 6 — Answer out loud: Machine Learning & Statistical Modeling

• Answer aloud, timed: How would you handle feature engineering for a model that needs to run in real-time?
• Answer aloud, timed: Explain the challenges of deploying a model that relies on streaming data versus batch data.

Deliverable: Spoken answers to 2 reported Machine Learning & Statistical Modeling question(s), under time.

Day 7 — Answer out loud: Behavioral & Collaboration

• Answer aloud, timed: Describe a time you had to explain a complex technical trade-off to a non-technical team member.
• Answer aloud, timed: How do you manage competing priorities when working on multiple high-impact projects?

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 optimize a machine learning pipeline to reduce latency.

Medium · Machine Learning & Statistical Modeling

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 explain a complex technical trade-off to a non-technical team member.

Medium · Behavioral & Collaboration

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 manage competing priorities when working on multiple high-impact projects?

Medium · Behavioral & Collaboration

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 time you took ownership of a critical system failure. How did you resolve it?

Medium · Behavioral & Collaboration

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 stay productive and maintain code quality in an environment that requires rapid iteration?

Medium · Behavioral & Collaboration

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?