As a Software Engineer at LiveEO GmbH, you are at the intersection of cutting-edge satellite technology and actionable infrastructure intelligence. The company leverages Earth observation data to provide critical monitoring solutions for global grid operators, railways, and pipeline companies. Your work directly impacts how these organizations manage risk, prevent outages, and optimize maintenance through automated, AI-driven insights. This role is both technically demanding and strategically significant. You will contribute to a product ecosystem that processes high-volume geospatial data, requiring a blend of robust backend engineering, scalable architecture, and a keen eye for user-centric frontend development. Whether you are building government solutions or core satellite processing pipelines, you are expected to translate complex technical requirements into reliable, high-performance software that operates at global scale.
Recruiter Screen
reportedInitial discussion to gauge interest in the company and high-level fit for the role.
What to demonstrate
- Initial discussion to gauge interest in the company and high-level fit for the role
- Depth in Software Engineering
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 Evaluations
reportedIncludes a take-home challenge or a live coding session to assess technical skills.
What to demonstrate
- Includes a take-home challenge or a live coding session to assess technical skills
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: Can you explain how you would handle the integration of external data APIs?
- Answer aloud and timed: What are the common trade-offs when choosing between different database architectures for geospatial data?
Work Review
reportedIn-depth review of the technical work submitted during the evaluations.
What to demonstrate
- In-depth review of the technical work submitted during the evaluations
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: Walk me through your most complex project; what were the primary technical hurdles?
- Answer aloud and timed: How do you approach writing a custom function to solve a data manipulation problem?
Final Meeting
reportedMeeting with leadership or team members to discuss final evaluations and fit.
What to demonstrate
- Meeting with leadership or team members to discuss final evaluations and fit
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: Describe a time you had to refactor legacy code to improve system stability.
- Answer aloud and timed: How do you prioritize tasks when faced with competing technical requirements?
PracHub editorial advice for the preparation topics above.
Research the mission
Be ready to discuss why you want to work for a company that uses satellite data to monitor infrastructure. Showing genuine interest in the business domain is a major plus.
Document your logic
During coding tasks, explain your reasoning out loud. Interviewers are often more interested in your problem-solving process than just the final result.
Prepare for follow-ups
If you use a specific technology or library, know it inside and out. You will likely face follow-up questions on why you chose it and what the alternatives were.
Focus on readability
Treat your interview code as if it were going into production. Clean code and clear naming conventions demonstrate professionalism.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Walk me through your most complex project; what were the primary technical hurdles?
Walk me through your most complex project; what were the primary technical hurdles?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
How do you approach writing a custom function to solve a data manipulation problem?
How do you approach writing a custom function to solve a data manipulation problem?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
How do you prioritize tasks when faced with competing technical requirements?
How do you prioritize tasks when faced with competing technical requirements?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
Write the update path that detects a concurrent edit
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?
How do you approach optimizing performance when processing large datasets?
How do you approach optimizing performance when processing large datasets?
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?
Can you explain how you would handle the integration of external data APIs?
Can you explain how you would handle the integration of external data APIs?
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 are the common trade-offs when choosing between different database architectures for geospatial data?
What are the common trade-offs when choosing between different database architectures for geospatial 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?
Every query on one table stalls for forty seconds mid-deploy
During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.
Approach
- Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
- Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
- Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
- Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.
Follow-up
- The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
- Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?
Built from the rounds and topics LiveEO GmbH candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the LiveEO GmbH loop
- Write out the reported sequence: Recruiter Screen, Technical Evaluations, Work Review, Final Meeting.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.
02Work Software Engineering
- Spend the session on Software Engineering, which LiveEO GmbH candidates report being tested on.
- Write one worked example in Software Engineering and time yourself on it.
Deliverable: One timed worked example in Software Engineering.
03Work Satellite Image Processing
- Spend the session on Satellite Image Processing, which LiveEO GmbH candidates report being tested on.
- Write one worked example in Satellite Image Processing and time yourself on it.
Deliverable: One timed worked example in Satellite Image Processing.
04Work Full-Stack Development
- Spend the session on Full-Stack Development, which LiveEO GmbH candidates report being tested on.
- Write one worked example in Full-Stack Development and time yourself on it.
Deliverable: One timed worked example in Full-Stack Development.
05Answer out loud: Technical and Domain Expertise
- Answer aloud, timed: What is your experience with the specific tech stack we use for our core products?
- Answer aloud, timed: How do you approach optimizing performance when processing large datasets?
Deliverable: Spoken answers to 2 reported Technical and Domain Expertise question(s), under time.
06Answer out loud: Project Experience and Problem Solving
- Answer aloud, timed: Walk me through your most complex project; what were the primary technical hurdles?
- Answer aloud, timed: How do you approach writing a custom function to solve a data manipulation problem?
Deliverable: Spoken answers to 2 reported Project Experience and Problem Solving question(s), under time.
07Dry run for LiveEO GmbH
- Run one full mock under time, then write down the two questions you most want to ask your interviewers.
Deliverable: A completed timed mock and two questions to ask.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
What is your experience with the specific tech stack we use for our core products?
What is your experience with the specific tech stack we use for our core products?
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 you had to refactor legacy code to improve system stability.
Describe a time you had to refactor legacy code to improve system stability.
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?
Estimate work you have never done and defend the range
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
Approach
- Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
- Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
- Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
- Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.
Follow-up
- How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
- Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?
- 01
What is your experience with the specific tech stack we use for our core products?
- 02
Describe a time you had to refactor legacy code to improve system stability.
- 03
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
How long does the entire interview process usually take?
The process generally takes more than a month from the initial application to a final decision. It involves multiple stages, including a significant technical challenge, so plan your schedule accordingly.
LiveEO GmbH Software Engineer candidate reports ↗What differentiates a successful candidate?
Successful candidates don't just write working code; they demonstrate a deep understanding of why their solution is optimal for the specific problem. They also communicate their thought process clearly throughout the technical challenge.
LiveEO GmbH Software Engineer candidate reports ↗Is the technical challenge difficult?
The challenge is designed to be comprehensive and can be time-consuming. It is meant to test your ability to build a functional, well-structured solution from the ground up.
LiveEO GmbH Software Engineer candidate reports ↗What is the culture like at LiveEO GmbH?
The culture is professional and mission-driven, focusing on innovation in the space-tech sector. You will be working in a collaborative environment where technical excellence and problem-solving are highly valued.
LiveEO GmbH Software Engineer candidate reports ↗What topics does LiveEO GmbH test in interviews?
LiveEO GmbH interviews most often cover Full-Stack Development, Frontend Development, Backend Development, Project Management, and Software Engineering. The exact emphasis depends on the specific role you apply for.
LiveEO GmbH Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01LiveEO GmbH Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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