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Ursa Space Systems Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at Ursa Space Systems plays a pivotal role in driving the technology that powers global geospatial intelligence. The engineering team is responsible for building and maintaining the sophisticated pipeline that ingests, processes, and analyzes massive datasets, including Synthetic Aperture Radar (SAR) and other satellite imagery. By translating complex radar data into actionable insights, software engineers directly impact customers in defense, intelligence, finance, and logistics who rely on real-time global monitoring. In this role, you will work on highly complex backend services, scalable data pipelines, and robust cloud infrastructure. The problems you will solve require a deep understanding of distributed systems, database optimization, and high-performance computing.

This guide is scoped to a Software Engineer candidate at Ursa Space Systems.

Focus areas

Data Modeling
Database Design
System Design

The Ursa Space Systems interview process

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

Automated Technical Assessment

Initial assessment to evaluate core coding and problem-solving abilities.

What to demonstrate

Initial assessment to evaluate core coding and problem-solving abilities
Depth in Data Modeling

How to prepare

Answer aloud and timed: Design the backend architecture and data models for a high-traffic social media platform, focusing specifically on backend business logic and database relations.
Answer aloud and timed: How would you structure a relational database schema to support complex, hierarchical data relationships?

HR and Technical Conversations

Discussions with human resources and senior technical leaders.

What to demonstrate

Discussions with human resources and senior technical leaders
Depth in Data Modeling

How to prepare

Answer aloud and timed: Explain how you would decouple a monolithic application into microservices to improve scalability and maintainability.
Answer aloud and timed: How do you handle database connection pooling and query optimization under high read/write loads?

Practical Engineering Exercises

Hands-on exercises to demonstrate engineering skills.

What to demonstrate

Hands-on exercises to demonstrate engineering skills
Depth in Data Modeling

How to prepare

Answer aloud and timed: Walk through a complex technical challenge from a past project and explain how you resolved it.
Answer aloud and timed: Why did you choose specific database technologies (e.g., SQL vs. NoSQL) in your previous roles?

Real-Time Collaboration

Focus on your ability to collaborate and discuss system design concepts.

What to demonstrate

Focus on your ability to collaborate and discuss system design concepts
Depth in Data Modeling

How to prepare

Answer aloud and timed: Describe your experience with cloud infrastructure and how you optimized resource utilization.
Answer aloud and timed: How do you approach writing automated tests for complex data processing pipelines?

Final Discussions

Engagements with upper-level management, including discussions on engineering strategy.

What to demonstrate

Engagements with upper-level management
Including discussions on engineering strategy

How to prepare

Answer aloud and timed: Describe a time when you had a technical disagreement with a team lead or architect. How did you handle it?
Answer aloud and timed: How do you manage your time and prioritize tasks when working on multiple high-priority engineering deliverables?

Where candidates lose points

Understand the domain

While prior geospatial experience is not always a hard requirement, having a basic understanding of satellite data, radar (SAR), and coordinate systems will help you stand out.

Be ready to talk about your resume

Expect detailed questions about your past projects. You should be able to explain the "why" behind your technical choices, not just the "how."

Over-communicate during pair programming

Treat the Lead Developer as a collaborator. Explain what you are doing, ask questions when stuck, and walk through your debugging process out loud.

Focus on data modeling

When designing systems, start with the data. Showing that you can structure clean, optimized database schemas is highly valued by the engineering team.

Questions & practice

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

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?

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
Where does the fingerprint get computed relative to request decompression and the body-size limit?

How would you structure a relational database schema to support complex, hierarchical data relationships?

Medium · System Design & Architecture

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?

Why did you choose specific database technologies (e.g., SQL vs. NoSQL) in your previous roles?

Medium · Technical & Resume Deep Dives

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?

Design the backend architecture and data models for a high-traffic social media platform, focusing specificall

Medium · System Design & Architecture

Design the backend architecture and data models for a high-traffic social media platform, focusing specifically on backend business logic and database relations.

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?

Explain how you would decouple a monolithic application into microservices to improve scalability and maintain

Medium · System Design & Architecture

Explain how you would decouple a monolithic application into microservices to improve scalability and maintainability.

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?

Walk through a complex technical challenge from a past project and explain how you resolved it.

Medium · Technical & Resume Deep Dives

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 do you approach writing automated tests for complex data processing pipelines?

Medium · Technical & Resume Deep Dives

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?

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 Ursa Space Systems candidates report.

Day 1 — Map the Ursa Space Systems loop

Write out the reported sequence: Automated Technical Assessment, HR and Technical Conversations, Practical Engineering Exercises, Real-Time Collaboration, Final Discussions.
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 Data Modeling

Spend the session on Data Modeling, which Ursa Space Systems candidates report being tested on.
Write one worked example in Data Modeling and time yourself on it.

Deliverable: One timed worked example in Data Modeling.

Day 3 — Work Database Design

Spend the session on Database Design, which Ursa Space Systems candidates report being tested on.
Write one worked example in Database Design and time yourself on it.

Deliverable: One timed worked example in Database Design.

Day 4 — Work System Design

Spend the session on System Design, which Ursa Space Systems 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 5 — Answer out loud: System Design & Architecture

Answer aloud, timed: Design the backend architecture and data models for a high-traffic social media platform, focusing specifically on backend business logic and database relations.
Answer aloud, timed: How would you structure a relational database schema to support complex, hierarchical data relationships?

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

Day 6 — Answer out loud: Technical & Resume Deep Dives

Answer aloud, timed: Walk through a complex technical challenge from a past project and explain how you resolved it.
Answer aloud, timed: Why did you choose specific database technologies (e.g., SQL vs. NoSQL) in your previous roles?

Deliverable: Spoken answers to 2 reported Technical & Resume Deep Dives question(s), under time.

Day 7 — Answer out loud: Behavioral & Collaboration

Answer aloud, timed: Describe a time when you had a technical disagreement with a team lead or architect. How did you handle it?
Answer aloud, timed: How do you manage your time and prioritize tasks when working on multiple high-priority engineering deliverables?

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.

How do you handle database connection pooling and query optimization under high read/write loads?

Medium · System Design & Architecture

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 your experience with cloud infrastructure and how you optimized resource utilization.

Medium · Technical & Resume Deep Dives

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 time when you had a technical disagreement with a team lead or architect. How did you handle it?

Medium · Behavioral & Collaboration

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 manage your time and prioritize tasks when working on multiple high-priority engineering deliverabl

Medium · Behavioral & Collaboration

How do you manage your time and prioritize tasks when working on multiple high-priority engineering deliverables?

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 explain a highly complex technical concept to a non-technical stakeholder.

Medium · Behavioral & Collaboration

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 constructive feedback on your code during peer reviews?

Medium · Behavioral & Collaboration

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?