As a Software Engineer at Statkraft, you will play a critical role in driving the global transition to green energy. Statkraft is Europe’s largest generator of renewable energy, and technology is at the very heart of its operations. The software you build and maintain directly impacts how wind, solar, and hydropower assets are managed, optimized, and integrated into the global energy grid. The engineering teams at Statkraft build high-performance data pipelines, algorithmic trading platforms, IoT integrations, and sophisticated market analysis tools. Your work will enable energy traders, portfolio managers, and operational teams to make real-time, data-driven decisions that balance energy supply and demand across multiple countries. The scale of data and the real-time nature of the energy markets present unique backend, architectural, and algorithmic challenges. This role is not just about writing clean code; it is about solving complex, multi-dimensional physical and financial problems. Whether you are optimizing hydro-reservoir scheduling or building low-latency trading APIs, your contributions will directly support a more sustainable and reliable energy future.
Initial Screening
reportedConversations to establish mutual alignment between the candidate and the organization.
What to demonstrate
- Conversations to establish mutual alignment between the candidate and the organization
- 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.
Technical Assessment
reportedRigorous evaluations of technical and analytical skills.
What to demonstrate
- Rigorous evaluations of technical and analytical skills
- Depth in Python
How to prepare
- Answer aloud and timed: What are decorators, and how would you implement a custom decorator for logging execution time?
- Answer aloud and timed: Explain the difference between multi-threading and multi-processing in the context of the Global Interpreter Lock (GIL).
System Design Evaluation
reportedIn-depth discussions to assess system design capabilities.
What to demonstrate
- In-depth discussions to assess system design capabilities
- Depth in Python
How to prepare
- Answer aloud and timed: How do you handle database migrations and connection pooling in a highly concurrent backend system?
- Answer aloud and timed: Given an array of integers representing historical energy prices, write an algorithm to find the maximum profit you could make by buying and selling a single asset.
Behavioral Evaluation
reportedAssessing cultural alignment and behavioral fit within the team.
What to demonstrate
- Assessing cultural alignment and behavioral fit within the team
- 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 evaluation above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Brush up on language fundamentals
If your team uses Python, ensure you are deeply familiar with core Python concepts, memory management, and common libraries. A live technical quiz or coding test is common in the early stages.
Structure your presentations clearly
If your process includes a business case presentation, focus on clarity, logical progression, and explicitly stating your architectural assumptions. Be prepared to defend your technical choices.
Prepare behavioral stories
Use the STAR method (Situation, Task, Action, Result) to structure your answers for behavioral and attitudinal rounds. Highlight collaboration, adaptability, and problem-solving.
Understand Statkraft's mission
Show a genuine interest in renewable energy and sustainability. Aligning your personal motivation with the company's core mission can set you apart from other candidates.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the difference between deep copy and shallow copy in Python.
Explain the difference between deep copy and shallow copy in Python.
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 does Python's Garbage Collector manage memory, and what are reference cycles?
How does Python's Garbage Collector manage memory, and what are reference cycles?
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?
What are decorators, and how would you implement a custom decorator for logging execution time?
What are decorators, and how would you implement a custom decorator for logging execution time?
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?
Explain the difference between multi-threading and multi-processing in the context of the Global Interpreter L
Explain the difference between multi-threading and multi-processing in the context of the Global Interpreter Lock (GIL).
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?
Given an array of integers representing historical energy prices, write an algorithm to find the maximum profi
Given an array of integers representing historical energy prices, write an algorithm to find the maximum profit you could make by buying and selling a single asset.
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?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
Implement a rate-limiter class that limits the number of API requests a user can make within a rolling time wi
Implement a rate-limiter class that limits the number of API requests a user can make within a rolling time window.
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?
Design an efficient data structure to store and query time-series energy generation data for multiple wind far
Design an efficient data structure to store and query time-series energy generation data for multiple wind farms.
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?
How would you design a scalable data ingestion pipeline to process real-time telemetry data from thousands of
How would you design a scalable data ingestion pipeline to process real-time telemetry data from thousands of wind turbines?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Walk us through how you would architect a system that processes and reconciles daily energy trading transactio
Walk us through how you would architect a system that processes and reconciles daily energy trading transactions from multiple external APIs.
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?
How do you ensure high availability and fault tolerance in a microservices architecture where data consistency
How do you ensure high availability and fault tolerance in a microservices architecture where data consistency is critical?
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 Statkraft candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Statkraft loop
- Write out the reported sequence: Initial Screening, Technical Assessment, System Design Evaluation, Behavioral Evaluation.
- 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 Python
- Spend the session on Python, which Statkraft candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
03Work Coding Interviews
- Spend the session on Coding Interviews, which Statkraft candidates report being tested on.
- Write one worked example in Coding Interviews and time yourself on it.
Deliverable: One timed worked example in Coding Interviews.
04Work Problem Solving
- Spend the session on Problem Solving, which Statkraft candidates report being tested on.
- Write one worked example in Problem Solving and time yourself on it.
Deliverable: One timed worked example in Problem Solving.
05Answer out loud: Core Language & Technical Concepts
- Answer aloud, timed: Explain the difference between deep copy and shallow copy in Python.
- Answer aloud, timed: How does Python's Garbage Collector manage memory, and what are reference cycles?
Deliverable: Spoken answers to 2 reported Core Language & Technical Concepts question(s), under time.
06Answer out loud: Coding & Algorithmic Problem-Solving
- Answer aloud, timed: Given an array of integers representing historical energy prices, write an algorithm to find the maximum profit you could make by buying and selling a single asset.
- Answer aloud, timed: Implement a rate-limiter class that limits the number of API requests a user can make within a rolling time window.
Deliverable: Spoken answers to 2 reported Coding & Algorithmic Problem-Solving question(s), under time.
07Answer out loud: System Design & Business Case Scenarios
- Answer aloud, timed: How would you design a scalable data ingestion pipeline to process real-time telemetry data from thousands of wind turbines?
- Answer aloud, timed: Walk us through how you would architect a system that processes and reconciles daily energy trading transactions from multiple external APIs.
Deliverable: Spoken answers to 2 reported System Design & Business Case Scenarios 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.
How do you handle database migrations and connection pooling in a highly concurrent backend system?
How do you handle database migrations and connection pooling in a highly concurrent backend system?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a time when you had to collaborate with stakeholders from different departments (e.g., energy traders
Describe a time when you had to collaborate with stakeholders from different departments (e.g., energy traders or risk analysts) to deliver a technical solution.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Tell me about a time you had to work on a codebase with minimal documentation. How did you navigate it?
Tell me about a time you had to work on a codebase with minimal documentation. How did you navigate it?
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 prioritize technical debt when working under tight business deadlines?
How do you prioritize technical debt when working under tight business deadlines?
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
How do you handle database migrations and connection pooling in a highly concurrent backend system?
- 02
Describe a time when you had to collaborate with stakeholders from different departments (e.g., energy traders or risk analysts) to deliver a technical solution.
- 03
Tell me about a time you had to work on a codebase with minimal documentation. How did you navigate it?
- 04
How do you prioritize technical debt when working under tight business deadlines?
How difficult is the interview process at Statkraft?
The difficulty is generally rated as average. While the technical questions and coding assessments are standard for modern software engineering roles, the process can feel rigorous due to the multiple rounds and the occasional requirement for business case presentations.
Statkraft Software Engineer candidate reports ↗How long does the entire recruitment process take?
The timeline can vary by location and team, but candidates typically report the process taking anywhere from 20 days to a few weeks. The company is generally communicative and keeps candidates informed of their status.
Statkraft Software Engineer candidate reports ↗Is there a heavy focus on the energy sector during the interviews?
While prior domain knowledge in energy trading or renewables is highly valued, it is not always a strict prerequisite. The interviewers are primarily looking for strong software engineering fundamentals and the analytical capability to learn the domain quickly.
Statkraft Software Engineer candidate reports ↗What is the working culture like for engineers?
The culture is highly professional, collaborative, and respectful. Statkraft emphasizes a healthy work-life balance and a supportive, stress-free interview environment where candidates are given ample time to think and respond.
Statkraft Software Engineer candidate reports ↗What topics does Statkraft test in interviews?
Statkraft interviews most often cover Python, SQL, Problem Solving, Probability, and Machine learning. The exact emphasis depends on the specific role you apply for.
Statkraft 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.
- 01Statkraft 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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