Sibanye Stillwater Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Sibanye Stillwater, you play a critical role in bridging cutting-edge digital technology with heavy industrial mining operations. This position is responsible for designing, building, and maintaining software solutions that optimize complex extraction processes, enhance data collection from subterranean environments, and ensure the safety and efficiency of operations across major facilities. Your work directly impacts how engineering and operational teams monitor real-time data, manage equipment maintenance, and streamline plant automation. The scope of this role encompasses diverse technical challenges, ranging from process control systems to robust internal tooling and data management applications.

This guide is scoped to a Software Engineer candidate at Sibanye Stillwater.

Focus areas

• Industrial/Mining Software Context
• Mine Engineering (Domain Modeling)
• Process Control Engineering (Automation Concepts)

The Sibanye Stillwater interview process

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

Initial Contact

Engagement with a recruiter to discuss your background and the role.

What to demonstrate

• Engagement with a recruiter to discuss your background and the role
• Depth in Industrial/Mining Software Context

How to prepare

• Answer aloud and timed: Can you walk through how you approach designing a software system that needs to interface with physical hardware or industrial telemetry?
• Answer aloud and timed: How do you handle data consistency and error recovery when dealing with intermittent network connectivity in remote operational environments?

Cultural Fit Assessment

Evaluation of alignment between your values and the company's culture.

What to demonstrate

• Evaluation of alignment between your values and the company's culture
• Depth in Industrial/Mining Software Context

How to prepare

• Answer aloud and timed: What experience do you have with process control systems, and how do you ensure high availability for mission-critical applications?
• Answer aloud and timed: How do you optimize software performance when processing large volumes of real-time sensor data?

Technical Dives

In-depth technical interviews assessing your problem-solving skills and technical knowledge.

What to demonstrate

• In-depth technical interviews assessing your problem-solving skills and technical knowledge
• Depth in Industrial/Mining Software Context

How to prepare

• Answer aloud and timed: What tools and frameworks do you typically rely on for building robust, scalable backend services?
• Answer aloud and timed: How would you architect a centralized logging and monitoring system for distributed mining or processing sites?

Stakeholder Involvement

Interviews involving stakeholders from both technology and operations departments.

What to demonstrate

• Interviews involving stakeholders from both technology and operations departments
• Depth in Industrial/Mining Software Context

How to prepare

• Answer aloud and timed: Describe a time when you had to design a system for scalability. How did you plan for future capacity and changing requirements?
• Answer aloud and timed: How do you approach database schema design when dealing with high-frequency time-series data from field equipment?

Final Decision

Review of all assessments and making the final hiring decision.

What to demonstrate

• Review of all assessments and making the final hiring decision
• Depth in Industrial/Mining Software Context

How to prepare

• Answer aloud and timed: What strategies do you use to decouple complex monolithic applications into maintainable microservices?
• Answer aloud and timed: How do you evaluate security risks and implement access controls in industrial software environments?

Where candidates lose points

Prepare concrete examples

Use the STAR method to structure your behavioral answers, focusing specifically on how you handled technical ambiguity and cross-functional collaboration.

Understand the domain

Familiarize yourself with the challenges of industrial data processing, telemetry, and high-availability systems to show immediate context awareness.

Ask thoughtful questions

Take advantage of the conversational interview style to ask about team culture, legacy system maintenance, and how engineering supports daily operations.

Focus on resilience

Emphasize your experience with error handling, data recovery, and graceful degradation during technical discussions.

Questions & practice

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

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

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

1. Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
2. Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
3. For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
4. Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

• The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
• Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

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

Approach

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?

How do you approach database schema design when dealing with high-frequency time-series data from field equipm

Medium · System Design and Architecture

How do you approach database schema design when dealing with high-frequency time-series data from field equipment?

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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?

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?

Can you walk through how you approach designing a software system that needs to interface with physical hardwa

Medium · Technical and Domain Knowledge

Can you walk through how you approach designing a software system that needs to interface with physical hardware or industrial telemetry?

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 experience do you have with process control systems, and how do you ensure high availability for mission-

Medium · Technical and Domain Knowledge

What experience do you have with process control systems, and how do you ensure high availability for mission-critical applications?

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 optimize software performance when processing large volumes of real-time sensor data?

Medium · Technical and Domain Knowledge

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 tools and frameworks do you typically rely on for building robust, scalable backend services?

Medium · Technical and Domain Knowledge

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 architect a centralized logging and monitoring system for distributed mining or processing sites

Medium · System Design and Architecture

How would you architect a centralized logging and monitoring system for distributed mining or processing sites?

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?

Describe a time when you had to design a system for scalability. How did you plan for future capacity and chan

Medium · System Design and Architecture

Describe a time when you had to design a system for scalability. How did you plan for future capacity and changing requirements?

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?

What strategies do you use to decouple complex monolithic applications into maintainable microservices?

Medium · System Design and Architecture

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 evaluate security risks and implement access controls in industrial software environments?

Medium · System Design and Architecture

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?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

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

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

Suggested practice checklist

Built from the rounds and topics Sibanye Stillwater candidates report.

Day 1 — Map the Sibanye Stillwater loop

• Write out the reported sequence: Initial Contact, Cultural Fit Assessment, Technical Dives, Stakeholder Involvement, Final Decision.
• 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 Industrial/Mining Software Context

• Spend the session on Industrial/Mining Software Context, which Sibanye Stillwater candidates report being tested on.
• Write one worked example in Industrial/Mining Software Context and time yourself on it.

Deliverable: One timed worked example in Industrial/Mining Software Context.

Day 3 — Work Mine Engineering (Domain Modeling)

• Spend the session on Mine Engineering (Domain Modeling), which Sibanye Stillwater candidates report being tested on.
• Write one worked example in Mine Engineering (Domain Modeling) and time yourself on it.

Deliverable: One timed worked example in Mine Engineering (Domain Modeling).

Day 4 — Work Process Control Engineering (Automation Concepts)

• Spend the session on Process Control Engineering (Automation Concepts), which Sibanye Stillwater candidates report being tested on.
• Write one worked example in Process Control Engineering (Automation Concepts) and time yourself on it.

Deliverable: One timed worked example in Process Control Engineering (Automation Concepts).

Day 5 — Answer out loud: Technical and Domain Knowledge

• Answer aloud, timed: Can you walk through how you approach designing a software system that needs to interface with physical hardware or industrial telemetry?
• Answer aloud, timed: How do you handle data consistency and error recovery when dealing with intermittent network connectivity in remote operational environments?

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

Day 6 — Answer out loud: System Design and Architecture

• Answer aloud, timed: How would you architect a centralized logging and monitoring system for distributed mining or processing sites?
• Answer aloud, timed: Describe a time when you had to design a system for scalability. How did you plan for future capacity and changing requirements?

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

Day 7 — Answer out loud: Behavioral and Cultural Alignment

• Answer aloud, timed: Tell me about a time when you had to collaborate with non-technical stakeholders, such as mine engineers or plant operators, to gather requirements.
• Answer aloud, timed: How do you handle disagreements on technical direction within your engineering team?

Deliverable: Spoken answers to 2 reported Behavioral and Cultural Alignment question(s), under time.

Behavioural preparation

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

How do you handle data consistency and error recovery when dealing with intermittent network connectivity in r

Medium · Technical and Domain Knowledge

How do you handle data consistency and error recovery when dealing with intermittent network connectivity in remote operational environments?

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 me about a time when you had to collaborate with non-technical stakeholders, such as mine engineers or pl

Medium · Behavioral and Cultural Alignment

Tell me about a time when you had to collaborate with non-technical stakeholders, such as mine engineers or plant operators, to gather requirements.

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 handle disagreements on technical direction within your engineering team?

Medium · Behavioral and Cultural Alignment

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 situation where a project faced unexpected technical roadblocks or shifting priorities. How did you

Medium · Behavioral and Cultural Alignment

Describe a situation where a project faced unexpected technical roadblocks or shifting priorities. How did you adapt?

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?

What steps do you take to maintain code quality and mentor junior developers while meeting tight project deadl

Medium · Behavioral and Cultural Alignment

What steps do you take to maintain code quality and mentor junior developers while meeting tight project deadlines?

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 approach transparent communication when discussing organizational challenges or project delays?

Medium · Behavioral and Cultural Alignment

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?