At Rystad Energy, a Software Engineer plays a pivotal role in bridging the gap between massive, complex global energy data and actionable business intelligence. As an independent energy research and business intelligence company, Rystad Energy relies heavily on its software systems to collect, process, analyze, and distribute high-fidelity data to clients worldwide. You will not just be writing code; you will be architecting the systems that power critical decision-making across the global energy, renewables, and clean-tech sectors. The impact of this position is felt directly across the company's core SaaS platforms, interactive dashboards, and proprietary data ingestion pipelines. The engineering team is responsible for harvesting vast amounts of unstructured data, structuring it through robust databases, and serving it via highly performant APIs. This requires building systems that are resilient, scalable, and capable of handling complex mathematical and quantitative calculations. For engineers who thrive on data-intensive challenges, this role offers an exceptionally rich problem space. You will work on sophisticated web scraping frameworks, design high-throughput APIs, and collaborate closely with analysts and domain experts. The engineering culture values technical rigor, clean code architecture, and a strong sense of ownership, making it an ideal environment for developers who want their work to have a tangible global footprint.
Initial Screening
reportedCandidates undergo a quantitative and coding test, which may include a fast-paced assessment or home assignment.
What to demonstrate
- Candidates undergo a quantitative and coding test, which may include a fast-paced assessment or home assignment
- Depth in Python
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Core Technical Rounds
reportedCandidates participate in practical rounds featuring machine coding, object-oriented design discussions, and API architecture deep dives.
What to demonstrate
- Candidates participate in practical rounds featuring machine coding, object-oriented design discussions, and API architecture deep dives
- Depth in Python
How to prepare
- Answer aloud and timed: Write a complete, runnable program that parses a complex JSON payload, applies a specific business logic filter, and handles potential edge cases like missing keys or malformed data.
- Answer aloud and timed: Explain the difference between multi-threading and multi-processing in Python, specifically in the context of CPU-bound versus I/O-bound tasks.
Live Case Study
reportedAn interactive round where candidates collaborate with interviewers to solve a realistic data engineering problem.
What to demonstrate
- An interactive round where candidates collaborate with interviewers to solve a realistic data engineering problem
- Depth in Python
How to prepare
- Answer aloud and timed: How do you implement custom decorators in Python, and what are their typical use cases?
- Answer aloud and timed: Design a RESTful API to serve real-time global energy production data. How do you handle pagination, filtering, and rate limiting?
Managerial Interview
reportedFocus on past experiences, team collaboration, problem-solving methodology, and cultural alignment with Rystad Energy.
What to demonstrate
- Focus on past experiences, team collaboration, problem-solving methodology, and cultural alignment with Rystad Energy
- Depth in Python
How to prepare
- Answer aloud and timed: Compare SQL and NoSQL databases. In what scenario would you choose MongoDB over PostgreSQL for storing energy asset metadata?
- Answer aloud and timed: How would you design a caching layer for an API endpoint that experiences high read traffic but has daily data updates?
Behavioral Interview
reportedDiscussion centered on communication skills and structured thinking throughout the interview process.
What to demonstrate
- Discussion centered on communication skills and structured thinking throughout the interview process
- Depth in Python
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral interview above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Prioritize OOP Concepts: Do not just memorize the definitions of the four pillars of OOP. Be ready to write clean, modular Python classes that actively demonstrate inheritance, encapsulation, polymorphism, and abstraction during your live coding rounds.
Going into the loop without having done this.
Be Fast and Structured in Case Studies: During case study rounds, time is of the essence. Structure your thoughts immediately, communicate your plan to the interviewer, and build a working baseline before diving into micro-optimizations.
Going into the loop without having done this.
In live coding rounds, always keep your camera on and be ready to share your screen. Talk through your thought process continuously so the interviewers can follow your logic even if you hit a syntax block.
Going into the loop without having done this.
Master API Design Principles: Spend time reviewing API design best practices. Be prepared to discuss how you would handle pagination, versioning, error handling, and security for a high-traffic endpoint.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the four pillars of Object-Oriented Programming (OOP) and how you have implemented them in a recent Py
Explain the four pillars of Object-Oriented Programming (OOP) and how you have implemented them in a recent Python project.
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 does Python manage memory, and what is the difference between deep copy and shallow copy?
How does Python manage memory, and what is the difference between deep copy and shallow copy?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Write a complete, runnable program that parses a complex JSON payload, applies a specific business logic filte
Write a complete, runnable program that parses a complex JSON payload, applies a specific business logic filter, and handles potential edge cases like missing keys or malformed data.
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?
Explain the difference between multi-threading and multi-processing in Python, specifically in the context of
Explain the difference between multi-threading and multi-processing in Python, specifically in the context of CPU-bound versus I/O-bound tasks.
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
How do you implement custom decorators in Python, and what are their typical use cases?
How do you implement custom decorators in Python, and what are their typical use cases?
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 would you optimize an algorithm that processes large matrices of energy production forecasts to minimize t
How would you optimize an algorithm that processes large matrices of energy production forecasts to minimize time complexity?
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?
Compare SQL and NoSQL databases. In what scenario would you choose MongoDB over PostgreSQL for storing energy
Compare SQL and NoSQL databases. In what scenario would you choose MongoDB over PostgreSQL for storing energy asset metadata?
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?
Walk through the process of designing a database schema for tracking historical carbon emissions across thousa
Walk through the process of designing a database schema for tracking historical carbon emissions across thousands of companies.
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 a RESTful API to serve real-time global energy production data. How do you handle pagination, filtering
Design a RESTful API to serve real-time global energy production data. How do you handle pagination, filtering, and rate limiting?
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?
How would you design a caching layer for an API endpoint that experiences high read traffic but has daily data
How would you design a caching layer for an API endpoint that experiences high read traffic but has daily data updates?
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?
How do you ensure backward compatibility when introducing breaking changes to an active production API?
How do you ensure backward compatibility when introducing breaking changes to an active production API?
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?
How would you design a scalable web scraping pipeline using Scrapy to crawl financial reports from hundreds of
How would you design a scalable web scraping pipeline using Scrapy to crawl financial reports from hundreds of energy company websites daily?
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?
What strategies do you employ to handle websites that use dynamic JavaScript rendering or anti-scraping mechan
What strategies do you employ to handle websites that use dynamic JavaScript rendering or anti-scraping mechanisms?
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?
How do you structure a data pipeline to validate, clean, and store scraped data into a relational database wit
How do you structure a data pipeline to validate, clean, and store scraped data into a relational database without causing bottlenecks?
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?
Describe a scenario where a scraper failed in production because of a target site layout change. How did you d
Describe a scenario where a scraper failed in production because of a target site layout change. How did you detect and resolve it?
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?
Solve a series of 10 quantitative and logical reasoning questions under a strict 30-minute time limit.
Solve a series of 10 quantitative and logical reasoning questions under a strict 30-minute time limit.
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?
Explain how you would validate the accuracy of a massive dataset after running it through an ETL pipeline.
Explain how you would validate the accuracy of a massive dataset after running it through an ETL pipeline.
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?
Listing latency scales with page size, not with filters
The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.
Approach
- Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
- Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
- Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
- Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.
Follow-up
- The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
- How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?
Built from the rounds and topics Rystad Energy candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Rystad Energy loop
- Write out the reported sequence: Initial Screening, Core Technical Rounds, Live Case Study, Managerial Interview, Behavioral Interview.
- 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.
02Work Python
- Spend the session on Python, which Rystad Energy candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
03Work API Design
- Spend the session on API Design, which Rystad Energy candidates report being tested on.
- Write one worked example in API Design and time yourself on it.
Deliverable: One timed worked example in API Design.
04Work Coding Tests (Algorithms/DS practice)
- Spend the session on Coding Tests (Algorithms/DS practice), which Rystad Energy candidates report being tested on.
- Write one worked example in Coding Tests (Algorithms/DS practice) and time yourself on it.
Deliverable: One timed worked example in Coding Tests (Algorithms/DS practice).
05Answer out loud: Python & Machine Coding
- Answer aloud, timed: Explain the four pillars of Object-Oriented Programming (OOP) and how you have implemented them in a recent Python project.
- Answer aloud, timed: How does Python manage memory, and what is the difference between deep copy and shallow copy?
Deliverable: Spoken answers to 2 reported Python & Machine Coding question(s), under time.
06Answer out loud: System & API Design
- Answer aloud, timed: Design a RESTful API to serve real-time global energy production data. How do you handle pagination, filtering, and rate limiting?
- Answer aloud, timed: Compare SQL and NoSQL databases. In what scenario would you choose MongoDB over PostgreSQL for storing energy asset metadata?
Deliverable: Spoken answers to 2 reported System & API Design question(s), under time.
07Answer out loud: Web Scraping & Ingestion Case Studies
- Answer aloud, timed: How would you design a scalable web scraping pipeline using Scrapy to crawl financial reports from hundreds of energy company websites daily?
- Answer aloud, timed: What strategies do you employ to handle websites that use dynamic JavaScript rendering or anti-scraping mechanisms?
Deliverable: Spoken answers to 2 reported Web Scraping & Ingestion Case Studies 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.
Unblock an engineer without taking the keyboard
A teammate has spent two days on a job handler that occasionally writes duplicate rows. They are certain the queue is delivering twice by mistake. You suspect a lease expiring under a slow handler, so the job is running concurrently with itself. Describe how you have unblocked someone in this position: what you asked before offering a hypothesis, what you showed them rather than told them, and what you left them owning. Then say what you would do if their theory turned out to be the right one.
Approach
- Ask before diagnosing, and ask for things answerable from data they already have: the attempt count on the job rows that produced duplicates, the handler's observed duration against its lease expiry, and whether the duplicate rows share a natural key that a unique constraint could have caught.
- Teach the shape rather than the answer. A lease cannot distinguish a dead worker from a slow one, so a handler that outruns its lease is running twice by design, and deploys deliver the other half by killing handlers mid-run on every rollout. Both of their candidate theories produce identical duplicate rows, which is why the evidence has to come from timings rather than from argument.
- Hand over a checklist they execute: a natural key on every write the handler performs so the second copy collides rather than appends, the record of intent written before any external effect, a lease heartbeat while running, and the metric that shows it working.
- Keep ownership with them deliberately. Pair on the first write, then step back; if you finish it yourself you have closed one ticket and left the same person stuck on the next redelivery.
Follow-up
- How would you distinguish a genuine double-delivery from a lease expiry using only the data already stored?
- Their handler calls an external endpoint before recording that it did. What do you tell them to change first?
Argue against a design, lose, and commit anyway
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
Approach
- State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
- Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
- Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
- Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.
Follow-up
- What threshold on that alert would have proved you right, and did anyone ever look at it?
- If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?
Narrate an outage you owned from page to postmortem
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
Approach
- Open on the signal rather than the cause: which metric at which percentile moved, on which service, at what time, so the listener follows the same evidence you had rather than a conclusion you already reached.
- Separate mitigation from diagnosis out loud. State what you did to stop the bleeding (flag off, shed traffic, drain a lease, roll back a deploy) and say plainly that you did it before the mechanism was known, because those are two jobs with different deadlines.
- Establish blast radius in countable terms: how many tenants, how many writes, and crucially whether the effect was loss or only delay. An append-only revision table or a pending outbox row means the change survived and the projection was merely behind, which is a repair rather than a data-loss incident.
- Prove the mechanism instead of asserting it. Name the trace span that grew, the plan that flipped to a sequential scan, the lease that expired, plus one alternative you ruled out and the signal that stayed flat while you ruled it out.
Follow-up
- What would you do differently in the first five minutes, given the same dashboard and no more information?
- Which follow-up action did you deliberately not take, and why was dropping it the right call?
- 01
A teammate has spent two days on a job handler that occasionally writes duplicate rows. They are certain the queue is delivering twice by mistake. You suspect a lease expiring under a slow handler, so the job is running concurrently with itself. Describe how you have unblocked someone in this position: what you asked before offering a hypothesis, what you showed them rather than told them, and what you left them owning. Then say what you would do if their theory turned out to be the right one.
- 02
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
- 03
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
How technical is the interview process at Rystad Energy?
The process is highly technical and practical. You will be evaluated on your live coding ability, your understanding of core computer science fundamentals (like OOP and API design), and your practical experience with databases and web scraping.
Rystad Energy Software Engineer candidate reports ↗What is the company culture like for Software Engineers?
The culture is highly collaborative, professional, and intellectually stimulating. Engineers work closely with domain experts and analysts, which provides a unique opportunity to learn about the global energy transition while building cutting-edge software.
Rystad Energy Software Engineer candidate reports ↗How should I prepare for the quantitative and case study rounds?
For the quantitative rounds, focus on speed and accuracy by practicing timed logical reasoning puzzles. For the case study rounds, practice designing complete data pipelines—from web scraping with Scrapy to structuring database tables—and be ready to explain your architectural choices.
Rystad Energy Software Engineer candidate reports ↗Is there flexibility in terms of remote or hybrid work?
Rystad Energy generally operates on a hybrid work model, blending the collaboration benefits of in-office teamwork with the flexibility of working from home. Specific arrangements can vary depending on the office location and team requirements.
Rystad Energy Software Engineer candidate reports ↗How hard is the Rystad Energy interview?
Candidates most commonly rate Rystad Energy interviews as medium, based on 104 reported interviews. About 35% of candidates who interview go on to receive an offer.
Rystad Energy Software Engineer candidate reports ↗What topics does Rystad Energy test in interviews?
Rystad Energy interviews most often cover Python, SQL, Problem Solving, Case Study Analysis, and Market Sizing. The exact emphasis depends on the specific role you apply for.
Rystad Energy Software Engineer candidate reports ↗Is Rystad Energy a good place to work?
Employees rate Rystad Energy 4.2 out of 5 overall, based on aggregated workplace reviews spanning career growth, work-life balance, compensation, culture, and management.
Rystad Energy Software Engineer candidate reports ↗Where is Rystad Energy headquartered?
Rystad Energy is headquartered in Oslo, Norway.
Rystad Energy Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Rystad Energy 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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