As a Software Engineer at SES, you are at the intersection of cutting-edge satellite communications and high-scale software systems. Your work directly impacts how SES delivers data, video, and connectivity services across the globe. You are not just writing code; you are building the digital infrastructure that enables reliable, global communication networks for government, maritime, aviation, and enterprise clients. The role involves significant complexity, as you will often be dealing with large datasets, real-time telemetry, and the unique challenges of satellite orbital mechanics and network traffic management. You will work within multidisciplinary teams—often collaborating with R&D, network operations, and satellite engineering—to solve problems that are often novel and mission-critical. Whether it is optimizing data pipelines or building the web applications that monitor satellite health, your contributions ensure that SES remains a leader in the global connectivity market. Expect an environment that values technical depth and domain understanding. You will be expected to understand how your software interacts with the physical hardware and the complex network systems that define the business model. SES
Talent Acquisition Screening
reportedInitial assessment to evaluate candidate alignment with the role.
What to demonstrate
- Initial assessment to evaluate candidate alignment with the role
- Depth in Programming Challenges / Coding Challenges
How to prepare
- Answer aloud and timed: How would you handle the integration of disparate data sources, such as satellite telemetry?
- Answer aloud and timed: Explain your experience with SQL and JavaScript in the context of building scalable web applications.
Technical Assessments
reportedIncludes live coding sessions, take-home assignments, or technical discussions.
What to demonstrate
- Includes live coding sessions, take-home assignments, or technical discussions
- Depth in Programming Challenges / Coding Challenges
How to prepare
- Answer aloud and timed: How does your experience in telecommunications or networking influence the way you design software architectures?
- Answer aloud and timed: Can you describe a time you had to optimize an algorithm for performance?
Meet with Hiring Managers
reportedDiscussion of long-term project goals and specific challenges of the role.
What to demonstrate
- Discussion of long-term project goals and specific challenges of the role
- Depth in Programming Challenges / Coding Challenges
How to prepare
- Prepare two projects you led end to end, each with the decision you owned and what it cost.
- Have three questions about the team's roadmap and how success is measured in the first six months.
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Be prepared to explain your resume: Know every project you have listed in detail and be ready to discuss the challenges you faced and how you overcame them.
Going into the loop without having done this.
Ask meaningful questions: Use the interview as an opportunity to learn about the team’s current challenges—this shows genuine interest and engagement.
Going into the loop without having done this.
Clarify the requirements: If a technical question feels ambiguous, do not hesitate to ask for clarification; it shows you value accuracy.
Going into the loop without having done this.
Stay professional: The interview process is also a test of your professional maturity; maintain a positive and collaborative attitude throughout.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Archive a resource graph without breaking live references or recursing
Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.
Approach
- Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
- Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
- Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
- Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.
Follow-up
- The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
- The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?
Identify the heaviest tenants in a five-minute window under memory pressure
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?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
Explain why the owner filter ignores the listing index
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
- 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.
- 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.
- 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.
- 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?
How would you handle the integration of disparate data sources, such as satellite telemetry?
How would you handle the integration of disparate data sources, such as satellite telemetry?
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 is your understanding of the network services provided by SES to its global customers?
What is your understanding of the network services provided by SES to its global customers?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How would you design a dashboard to visualize real-time satellite data on a map?
How would you design a dashboard to visualize real-time satellite data on a map?
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?
If you were tasked with merging two obscure data sources, what steps would you take to ensure data integrity?
If you were tasked with merging two obscure data sources, what steps would you take to ensure data integrity?
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?
One customer endpoint stalls deliveries to every other destination
The egress service delivers about 1.5k webhooks/second across 40,000 destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. Throughput falls to 300/second, queue depth climbs, and p99 delivery latency for unaffected destinations goes from 200 ms to minutes, while the error rate barely moves. One tenant holds 900 destination rows whose URLs share a hostname that now answers in 9.5 seconds. Explain the mechanism with the arithmetic, then give the containment in the order you would apply it.
Approach
- Look at saturation before errors. A flat error rate with collapsing throughput says nothing is failing, things are waiting, so the first signal to pull is in-flight request count or pool wait time rather than the error counter. This is the distinction that decides the whole investigation.
- Group in-flight work by resolved host, not by destination id. The cap is keyed per destination row, so 900 rows sharing one hostname buy 3,600 concurrent slots against a single host, each held for 9.5 seconds. The bulkhead was never a bulkhead for that host, and grouping by the wrong dimension is why the dashboard looked healthy.
- Do the arithmetic in both directions. Required concurrency is arrival rate times latency, so 1.5k/second at 200 ms needs about 300 in flight, which is entirely consumed by 3,600 slow slots; conversely whatever concurrency is left sustains rate equals concurrency divided by 9.5 seconds, which is the 300/second you are seeing. Matching both numbers is what promotes this from a plausible story to the mechanism.
- Explain why the circuit breaker never helped. It opens on consecutive failures, and a 9.5-second response inside a 10-second timeout is a success. Slow is not failing, so an error-rate breaker cannot see this; you need a slow-call ratio, a deadline propagated from the caller's remaining budget, or a concurrency limiter.
Follow-up
- The host recovers to 80 ms. How long does the queue take to drain, and what does the drain do to the recovered host?
- Where should the 10-second timeout number actually come from?
Built from the rounds and topics SES candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the SES loop
- Write out the reported sequence: Talent Acquisition Screening, Technical Assessments, Meet with Hiring Managers.
- 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 3 reported rounds, with the weakest marked.
02Work Programming Challenges / Coding Challenges
- Spend the session on Programming Challenges / Coding Challenges, which SES candidates report being tested on.
- Write one worked example in Programming Challenges / Coding Challenges and time yourself on it.
Deliverable: One timed worked example in Programming Challenges / Coding Challenges.
03Work Algorithms
- Spend the session on Algorithms, which SES candidates report being tested on.
- Write one worked example in Algorithms and time yourself on it.
Deliverable: One timed worked example in Algorithms.
04Work Data Integration / Merging Data Sources
- Spend the session on Data Integration / Merging Data Sources, which SES candidates report being tested on.
- Write one worked example in Data Integration / Merging Data Sources and time yourself on it.
Deliverable: One timed worked example in Data Integration / Merging Data Sources.
05Answer out loud: Technical & Domain Knowledge
- Answer aloud, timed: How would you handle the integration of disparate data sources, such as satellite telemetry?
- Answer aloud, timed: Explain your experience with SQL and JavaScript in the context of building scalable web applications.
Deliverable: Spoken answers to 2 reported Technical & Domain Knowledge question(s), under time.
06Answer out loud: Behavioral & Leadership
- Answer aloud, timed: How do you handle team management, and what is the largest team you have led?
- Answer aloud, timed: Describe a situation where you had to explain a complex technical issue to a non-technical stakeholder.
Deliverable: Spoken answers to 2 reported Behavioral & Leadership question(s), under time.
07Answer out loud: Problem Solving & System Design
- Answer aloud, timed: How would you design a dashboard to visualize real-time satellite data on a map?
- Answer aloud, timed: Describe your process for conducting a code review and identifying potential bottlenecks or security risks.
Deliverable: Spoken answers to 2 reported Problem Solving & System Design question(s), under time.
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.
Explain your experience with SQL and JavaScript in the context of building scalable web applications.
Explain your experience with SQL and JavaScript in the context of building scalable web applications.
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 does your experience in telecommunications or networking influence the way you design software architectur
How does your experience in telecommunications or networking influence the way you design software architectures?
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?
Can you describe a time you had to optimize an algorithm for performance?
Can you describe a time you had to optimize an algorithm for performance?
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 team management, and what is the largest team you have led?
How do you handle team management, and what is the largest team you have led?
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 technical issue to a non-technical stakeholder.
Describe a situation where you had to explain a complex technical issue to a non-technical stakeholder.
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 react when you receive feedback on your code or design choices?
How do you react when you receive feedback on your code or design choices?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
What has been your experience working with teams distributed across different international locations?
What has been your experience working with teams distributed across different international locations?
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 process for conducting a code review and identifying potential bottlenecks or security risks.
Describe your process for conducting a code review and identifying potential bottlenecks or security risks.
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?
- 01
Explain your experience with SQL and JavaScript in the context of building scalable web applications.
- 02
How does your experience in telecommunications or networking influence the way you design software architectures?
- 03
Can you describe a time you had to optimize an algorithm for performance?
- 04
How do you handle team management, and what is the largest team you have led?
How long does the interview process typically take?
The process can range from a few weeks to over a month, depending on the team and the number of interview rounds.
SES Software Engineer candidate reports ↗Should I expect a take-home assignment?
It is common for some teams to request a take-home assignment followed by a discussion on your design choices; be prepared to justify the trade-offs you made.
SES Software Engineer candidate reports ↗Is knowledge of satellite technology required?
While prior experience in the satellite industry is a significant plus, it is not always a strict requirement; a strong engineering background and a willingness to learn the domain are often sufficient.
SES Software Engineer candidate reports ↗What is the most important thing to focus on during the interview?
Focus on your communication. The ability to explain your technical decisions clearly and show how you work within a team is often the deciding factor for candidates.
SES Software Engineer candidate reports ↗How hard is the SES interview?
Candidates most commonly rate SES interviews as medium, based on 90 reported interviews. About 58% of candidates who interview go on to receive an offer.
SES Software Engineer candidate reports ↗What topics does SES test in interviews?
SES interviews most often cover SQL, JavaScript, Data Analysis, Technical Interviewing, and Python. The exact emphasis depends on the specific role you apply for.
SES Software Engineer candidate reports ↗Is SES a good place to work?
Employees rate SES 3.8 out of 5 overall, based on aggregated workplace reviews spanning career growth, work-life balance, compensation, culture, and management.
SES Software Engineer candidate reports ↗Where is SES headquartered?
SES is headquartered in Betzdorf, Luxembourg.
SES Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01SES Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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