Avangrid Software Engineer Interview Guide

The 60-second brief

Avangrid provides energy and utility services to customers in the United States, and transmission planning is one of the areas named in the Software Engineer role. According to candidates, engineers in this role design, build and maintain software used by internal teams and by outside customers. The day-to-day work they describe includes gathering requirements with cross-functional teams, taking part in code reviews, and using user feedback and performance metrics to choose what to improve next. Regulatory requirements and industry standards shape what gets shipped, so expect to talk about correctness, traceability and careful change as much as about new features.

This guide is for engineers interviewing for a Software Engineer role at Avangrid who work in Java, C# or Python, the languages in the requirements candidates describe. It covers both reported rounds and the four groups of reported questions: technical concepts, coding, problem-solving cases and behavioral. It also covers the scalable-web-application and query-optimization topics candidates mention, plus the basic power-sector vocabulary that appears in topic tags for this role.

Candidates report two rounds over roughly 2-4 weeks. First comes an Initial Screening on basic qualifications and fit, then a Technical Discussion that goes deeper on specific skills. Candidates describe behavioral and technical questions mixed together in a conversational format.

Focus areas

• Transmission Planning
• Substation Grounding
• Lightning Protection

Overview

Avangrid provides energy and utility services across the United States, and its Software Engineers build and maintain the applications behind those services. Transmission planning is named as one area of the work. Candidates describe a job that is mostly about existing systems: collecting requirements with other teams, reviewing code, and using feedback and performance data to decide what to change, all under regulatory and industry-standard constraints. For your preparation, that means the questions on maintainability, debugging and improving an existing system deserve as much practice as the algorithms.

The reported questions fall into four groups. The technical concept questions are how you would debug a complex issue, how synchronous and asynchronous programming differ, how you keep code maintainable and scalable, and what RESTful APIs are and how you have used them. The coding questions are three classics: reverse a linked list, implement binary search and state its complexity, and find two numbers in an array that sum to a target. The problem-solving cases are a project falling behind schedule, improving an existing system, and acting on user feedback. The behavioral questions cover working under pressure, your part in a team project, prioritizing several deadlines, a conflict with a teammate, and why you want to work in energy. Candidates also mention designing a scalable web application and optimizing a database query as example topics.

The requirements candidates describe list Java, C# or Python, Agile or Scrum experience, and a solid base in data structures and algorithms as must-haves. AWS or Azure, DevOps with CI/CD pipelines, and earlier energy or utility software work are nice-to-haves. Pick one of the three languages and prepare everything in it. Write the coding questions in that language and learn its concurrency model (asyncio in Python, async/await on Task in C#, CompletableFuture or virtual threads in Java). The sync-versus-async question is much easier to answer concretely when you can talk about your own runtime.

Topic tags on reports for this role also include transmission planning, substation grounding, lightning protection and IEEE standards knowledge. These are power-engineering subjects, not software ones. Energy experience is only a nice-to-have, so aim for vocabulary rather than depth: be able to define each term in a sentence and say why a utility cares about it. That knowledge also helps your answer on why you want to work in energy. Candidate accounts of the atmosphere vary. Some describe relaxed, open conversations, while others report problems with communication and professionalism, so confirm the next steps and timing in writing after each round.

The Avangrid interview process

Candidates report two rounds over roughly 2-4 weeks: an Initial Screening followed by a Technical Discussion. Neither round comes with a known question list, so the reported questions are prepared below by type, not by round.

Initial Screening

Candidates describe the Initial Screening as the first stage. It checks basic qualifications and fit before the deeper technical conversation. Treat it as the place to show that your background matches the requirements candidates describe (Java, C# or Python, Agile or Scrum, data structures and algorithms) and that you have a real reason to work at an energy and utility company. Keep your answers concrete and short, and use the end of the call to ask what the Technical Discussion covers and who will be in it.

What to demonstrate

• Whether your experience matches the listed must-haves: Java, C# or Python, Agile or Scrum, and data structures and algorithms
• Your motivation for working in the energy and utility sector
• How clearly you summarize your background and your projects

How to prepare

• Write a short walkthrough of your résumé that names, for each role, the language you used (Java, C# or Python), your delivery process (Scrum, Kanban) and one result you can back with a number.
• Prepare a specific answer to the energy-sector motivation question that mentions grid reliability, transmission planning or regulated software, and not just sustainability in general.
• Have one or two cloud or CI/CD examples ready (AWS, Azure, a pipeline you built or fixed), because those are the nice-to-haves candidates report.
• List three questions for the screener: what the Technical Discussion covers, whether it includes live coding, and the expected timeline.

Technical Discussion

Candidates describe the Technical Discussion as an in-depth interview on specific skills and competencies, run conversationally, with behavioral questions mixed in alongside the technical ones. A conversation-style technical stage is where the reported concept questions (debugging, sync versus async, REST, maintainability) and project deep-dives get discussed, and candidates also report classic coding questions for this role. Prepare to explain your reasoning aloud, to write small functions without an IDE, and to go two or three levels deep on any project you mention.

What to demonstrate

• Depth on the technical skills and projects on your résumé
• Reasoning explained aloud in a conversational format
• Correct, working solutions to standard coding problems when asked

How to prepare

• Practise saying your answers aloud to the four technical concept questions: debugging a complex issue, synchronous versus asynchronous programming, maintainable and scalable code, and RESTful APIs, each with an example from your own work.
• Write the linked-list reversal, binary search and two-sum solutions by hand in your chosen language, state each one's time and space complexity, and test the edge cases out loud.
• Pick two projects you can take apart end to end: the design, a trade-off you rejected, a production problem, and what you would change.
• Prepare a short outline for designing a scalable web application and a step-by-step method for optimizing a slow database query (EXPLAIN plan, indexes, N+1 queries), since candidates mention both.

Where candidates lose points

Losing the rest of the list in the linked-list reversal by overwriting curr.next before saving it, or returning head instead of prev.

Write the four pointer moves in a fixed order (save next, point back, advance prev, advance curr), then trace an empty list, one node and three nodes on paper before you say you are done.

An off-by-one or infinite loop in binary search, caused by mixing inclusive and half-open bounds or by setting lo = mid on a half-open range.

Choose one invariant, say it aloud, and test arrays of size 0, 1 and 2 with targets missing below, above and between the elements. Use lo + (hi - lo) / 2 in Java or C#.

Answering the synchronous-versus-asynchronous question with textbook definitions only, with no runtime detail and no example from your own language.

Prepare one example in your chosen language (asyncio, C# Task, or CompletableFuture), explain what happens when a blocking call runs inside an event loop, and say when you would use threads or processes instead.

Answering the maintainability, debugging and system-improvement questions in the abstract, with no system, no numbers and no tool you actually used.

Attach one real example to each: a refactor and what it removed, a bug you traced and how you found the cause, and a baseline you measured before and after a change.

Behavioral stories told as 'we' throughout, and an energy-motivation answer that repeats sustainability slogans.

Rewrite each story so the Action section starts with 'I'. Base the motivation answer on something specific: grid reliability, transmission planning, regulated software, or your own related work.

Questions & practice

What are the key differences between synchronous and asynchronous programming?

Medium · Technical / Domain Questions

Approach

1. Synchronous code blocks the caller until the operation finishes. Control flow and stack traces are simple, but the thread does nothing while it waits on I/O. Asynchronous code starts the operation and carries on, and the result comes back through a callback, future or promise, or async/await on an event loop.
2. Separate concurrency from parallelism. Async helps I/O-bound work (HTTP calls, database queries, reading from many devices) by not blocking while waiting. It does not speed up CPU-bound work, which needs threads or processes (in Python, multiprocessing, because of the GIL).
3. Name your language's model: asyncio and await in Python, Task with async/await in C#, CompletableFuture or virtual threads in Java. Explain that a blocking call inside an event loop (time.sleep in asyncio, .Result or .Wait() on a C# Task) stalls every other task and can deadlock.
4. Cover the costs: harder debugging, error handling for tasks nobody awaits, ordering guarantees, and backpressure when producers outrun consumers.
5. Extend it to system level: a synchronous REST call versus asynchronous messaging over a queue. The queue gives decoupling and retries in exchange for eventual consistency and an extra component to run.

Follow-up

• What happens if you call a blocking database driver inside an async request handler?
• How would you fan out 50 HTTP calls but cap them at 10 in flight?
• When would you choose threads over async I/O?

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

1. Iterative version: prev = null and curr = head. Loop while curr is not null: save next = curr.next, set curr.next = prev, then prev = curr and curr = next. Return prev. This is O(n) time and O(1) extra space.
2. Recursive version: reverse the rest of the list, then set head.next.next = head and head.next = null, and return the new head. It uses O(n) stack space, which can overflow on long lists (Python's default recursion limit is about 1000).
3. Cover the edge cases out loud: an empty list, a single node and two nodes. Trace three nodes by hand to show the pointers.
4. Explain that the next pointer must be saved before it is overwritten, otherwise the rest of the list is lost. For a doubly linked list, swap prev and next on each node.

Follow-up

• Reverse only the nodes between positions m and n in one pass.
• Reverse the list in groups of k.
• Use reversal to check whether a linked list is a palindrome in O(1) extra space.

How would you implement a binary search algorithm? Explain its time complexity.

Medium · Coding / Algorithms

Approach

1. State the precondition: the array is sorted, or more generally the predicate is monotonic. Keep lo and hi, compute mid = lo + (hi - lo) / 2 (which avoids int overflow in Java and C#), compare, and drop the half that cannot contain the target.
2. Complexity: each step halves the range, so it is O(log n) time. The iterative version uses O(1) space and the recursive one O(log n) stack.
3. Choose one loop invariant and keep to it: inclusive [lo, hi] with while lo <= hi, or half-open [lo, hi) with while lo < hi. Mixing them causes off-by-one errors and infinite loops. Test on arrays of size 0, 1 and 2, and on targets below, above and between the elements.
4. Know the variants and library calls: lower and upper bound for the first or last occurrence and the insertion point, binary search on the answer (smallest capacity that works), and a rotated sorted array. Python's bisect, Java's Arrays.binarySearch and C#'s Array.BinarySearch all encode a missing target's insertion point in the return value.

Follow-up

• Return the first occurrence when there are duplicates.
• Search a sorted array that has been rotated.
• When is a linear scan better than sorting and then binary searching?

Given an array of integers, find two numbers that add up to a specific target.

Medium · Coding / Algorithms

Approach

1. Clarify first: are indices or values returned, can the same element be used twice, are there duplicates and negatives, and what happens if there is no answer. Name the O(n^2) brute force over all pairs.
2. Single pass with a hash map: for each index i and value x, if target - x is already in the map, return its stored index and i. Otherwise store x -> i. Checking before inserting stops an element pairing with itself. This is O(n) time and O(n) space.
3. Alternative: sort (keeping the original indices if they must be returned), then move two pointers inward based on the sum. This is O(n log n) time, with O(1) extra space if only values are needed.
4. Edge cases: [3, 3] with target 6, negative numbers, an empty or single-element array, and int overflow when summing in Java or C# (use long).

Follow-up

• Return all unique pairs that sum to the target.
• Extend it to three numbers summing to the target.
• What changes if the numbers arrive as a stream or do not fit in memory?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

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

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

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

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

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

Discuss a project where you implemented a significant software feature. What challenges did you face?

Medium · Technical / Domain Questions

Approach

1. Pick a feature you owned end to end. Describe the problem, the users and the constraints in two sentences, then spend most of the answer on what you built and why.
2. Walk through the design: components, data model, API surface. Name one alternative you rejected and the reason (cost, risk, a deadline, an existing system you had to integrate with).
3. Give one technical challenge (a performance limit, a data migration, a legacy integration, a flaky dependency) and one non-technical one (a requirement change, a cross-team dependency). For each, say what you personally did, using 'I'.
4. Close with a measurable result (latency, defect rate, adoption, hours saved) and one thing you would do differently.
5. The common mistake is a team story where your own part is unclear, or a list of technologies with no decision behind them.

Follow-up

• What would break first if usage grew tenfold?
• How did you test it and roll it out safely?
• Which trade-off would you revisit now?

How do you ensure your code is maintainable and scalable?

Medium · Technical / Domain Questions

Approach

1. Split the question in two. Maintainability is about the next engineer: small cohesive modules, clear names, dependency injection so units can be tested in isolation, and consistent error handling. Scalability is about load: algorithmic complexity, data access and how state is held.
2. For maintainability, name the safety net: unit and integration tests, linting and static analysis, code review, and CI gates that block merges. Give one example of a refactor you did and what it removed (duplication, a god class, hidden coupling).
3. For scalability, cite concrete techniques: stateless services so you can scale horizontally, indexes and fixing N+1 queries, pagination, caching with a clear invalidation rule, and moving heavy work onto a queue.
4. Mention operability as part of maintainability: logging, metrics and a README or decision record, so someone else can run and change the system.
5. State the trade-off: design for load you have measured or expect, not imagined load, and say how you would know when it is time to scale.

Follow-up

• How do you decide between refactoring now and shipping first?
• Tell me about code that did not scale and what you changed.
• How do you keep standards consistent across a team?

Can you explain the concept of RESTful APIs and how you have used them in your projects?

Medium · Technical / Domain Questions

Approach

1. Define REST by its constraints: resources identified by URIs, a uniform interface over HTTP, stateless requests, cacheable responses, and a layered client-server setup, usually with JSON representations.
2. Get the method semantics right. GET is safe and idempotent. PUT replaces and is idempotent. PATCH updates part of a resource. POST creates and is not idempotent. DELETE is idempotent. Use matching status codes: 200, 201 with a Location header, 204, 400, 401 versus 403, 404, 409, 422, 500.
3. Show design choices: plural noun paths with nesting (/orders/{id}/items), filtering and pagination (cursor versus offset), versioning in the path or a header, a consistent error body, and authentication with OAuth2 or JWT plus rate limiting.
4. Tie it to your own work: an API you built or consumed, how you handled timeouts and retries, and how you made POST safe to retry with an idempotency key.
5. Briefly contrast REST with RPC-style or GraphQL APIs, and say when you would pick each.

Follow-up

• How would you make a POST that creates a payment or order safe to retry?
• When do you use PUT and when PATCH?
• How do you change an API without breaking existing clients?

Describe how you would approach a situation where a software project is falling behind schedule.

Medium · Problem-Solving / Case Studies

Approach

1. Measure the gap before acting: remaining scope against the team's real velocity, and the cause, whether that is underestimation, scope creep, a blocked dependency or lost people.
2. Lay out the options: cut or defer scope (MoSCoW), ship a usable slice first, remove blockers, move the date, or add people. Note that adding people late often slows a project down at first because of onboarding and coordination cost.
3. Tell stakeholders early, with options and their trade-offs, rather than only reporting bad news at the deadline.
4. Protect quality. Do not drop tests or reviews to catch up, and log any shortcut as tracked technical debt.
5. Prevent it happening again: run a retrospective on the estimates, use smaller increments, and track progress so slippage shows up earlier.

Follow-up

• The stakeholder refuses to cut any scope. What now?
• When does adding engineers actually help?
• What early signals tell you a project is slipping?

If tasked with improving an existing software system, what steps would you take?

Medium · Problem-Solving / Case Studies

Approach

1. Understand before changing: talk to users and operators, read the code and any docs, and record a baseline (latency, error rate, defect count, deploy frequency, support tickets).
2. Rank the problems using that data, then choose improvements by impact against risk instead of rewriting whatever looks ugliest.
3. Build a safety net first: characterization tests around current behavior, CI, and monitoring on the paths you will touch.
4. Change things in small steps: small pull requests, feature flags, and the strangler-fig pattern to replace parts behind a stable interface. Explain why a big-bang rewrite is risky.
5. Measure against the baseline afterwards and report the result.

Follow-up

• When is a full rewrite justified?
• There are no tests and the original authors have left. Where do you start?
• How do you get buy-in for improvements that bring no new features?

Present a case where you had to analyze user feedback to improve a product feature.

Medium · Problem-Solving / Case Studies

Approach

1. Name the sources: support tickets, surveys, usage analytics, app reviews, and conversations with internal users such as operators or field staff.
2. Group the feedback into themes and count frequency and severity, so a few loud complaints are not mistaken for a widespread problem. Check each theme against usage metrics.
3. Find the root cause behind the theme, define a success metric, ship the change gradually (staged rollout or A/B test), and compare before and after.
4. Tell it as a STAR story with one concrete feature and a before-and-after number, and say how you reported back to the users who raised it.

Follow-up

• Two user groups want opposite changes. How do you decide?
• What if the feedback contradicts your usage data?
• Tell me about feedback you decided not to act on.

Explain how you would approach debugging a complex software issue.

Medium · Technical / Domain Questions

Approach

1. Reproduce it first: pin down the exact inputs, environment, build version and data that trigger it, collect the stack trace and logs, and turn the reproduction into a failing test if you can.
2. Narrow the search space by asking what changed: recent deploys, config, dependency versions, data volume. Then bisect, using git bisect across commits or by cutting the pipeline in half and checking the intermediate output.
3. Add instrumentation before you trust a theory: structured logs with a correlation id across services, metrics around the failing path, and a debugger or breakpoints locally. Intermittent failures usually point to concurrency, timeouts, caching, retries or time zones.
4. Test one hypothesis at a time with an experiment that could disprove it, and keep a short log of what you ruled out, so the investigation can be handed over if it runs long.
5. If production is affected, separate mitigation (roll back, feature flag, failover) from the root-cause fix. Then add a regression test and write up the cause, the fix and what detection would have caught it earlier.

Follow-up

• The bug only happens in production and you cannot reproduce it locally. What do you do next?
• It hits roughly one request in a thousand. How do you narrow that down?
• How do you decide between rolling back and shipping a hot fix?

Suggested practice checklist

Each day is built from Avangrid's two reported rounds, its reported questions and the stack in the requirements candidates describe. Coding and concept work come first, followed by cases and behavioral stories, and the week ends with a full mock Technical Discussion.

Day 1 — Screening readiness and the energy-sector answer

• Write your résumé walkthrough, naming the language (Java, C# or Python), the delivery process and one measured result for each role.
• Write and say aloud your answer to why you want to work in the energy sector, built on one concrete reason and one link to your own experience.
• Write a one-line definition each for transmission planning, substation grounding, lightning protection and IEEE standards, and why a utility cares about them.
• List the questions you will ask in the Initial Screening about the Technical Discussion format and timeline.

Deliverable: A written résumé walkthrough, a rehearsed energy-motivation answer, a four-term power-sector glossary and a list of screening questions.

Day 2 — The three reported coding problems

• Write the linked-list reversal both iteratively and recursively in your chosen language, and trace empty, one-node and three-node inputs.
• Write binary search with an inclusive invariant, then a lower-bound version, and test sizes 0, 1 and 2 and missing targets.
• Write two-sum with a hash map and with sort plus two pointers, and test [3, 3] with target 6, negative numbers and a case with no answer.
• Say the time and space complexity of each solution aloud without notes.

Deliverable: Five working functions with test cases and a one-line complexity note for each.

Day 3 — Async runtimes and REST design

• Write a small program in your language that makes 20 I/O calls sequentially and then concurrently with a concurrency cap, and note the difference in wall-clock time.
• Write down what happens when a blocking call runs inside your runtime's event loop, and how to fix it.
• Write a REST outline for a simple task-tracking service: resource paths, methods, status codes, pagination, versioning, and an idempotency key on POST.

Deliverable: A runnable sync-versus-async comparison, a written note on blocking calls and a one-page REST API outline.

Day 4 — Debugging, maintainability and query tuning

• Write a debugging walkthrough of a real bug you fixed: reproduction, what changed, the bisect, the instrumentation, the root cause and the regression test.
• Prepare your maintainability-and-scalability answer with one refactor example and one scaling fix from your own work.
• Work through the practice question on why an owner filter ignores an index, then write your general method for optimizing a slow query (EXPLAIN, indexes, N+1 queries, pagination).

Deliverable: A written debugging story, a two-example maintainability answer and a query-optimization checklist.

Day 5 — Problem-solving cases and a web-app outline

• Answer the behind-schedule case aloud: measure the gap, list the options, communicate them, and prevent a repeat.
• Answer the improving-an-existing-system case with a baseline, characterization tests and a strangler-fig rollout.
• Prepare the user-feedback case as a STAR story with a before-and-after metric.
• Sketch an outline for a scalable web application: load balancer, stateless app tier, database with indexes and read replicas, cache and a job queue.

Deliverable: Three rehearsed case answers and a one-page scalable web application outline.

Day 6 — Behavioral story bank

• Write STAR stories for working under pressure, your part in a team project, prioritizing several deadlines, and a conflict with a teammate.
• Prepare the significant-feature project story with its design, one rejected alternative, two challenges and a measured result.
• Reread every story and change 'we' to 'I' wherever the action was yours.

Deliverable: Five STAR stories in the first person, each with a measurable result.

Day 7 — Full mock Technical Discussion

• Ask a friend to run a conversational mock that mixes one coding problem, one concept question and one behavioral question, all answered aloud.
• Write the coding solution without an IDE, then check it against your tests from day 2.
• Note every answer that ran long or lacked an example and rewrite it.

Deliverable: A mock-session log listing the weak answers and their rewritten versions.

Behavioural preparation

Candidates report behavioral questions mixed in with technical ones rather than kept to a separate stage. The reported prompts cover pressure, your part in a team, prioritizing deadlines, conflict with a teammate and your motivation for the energy sector. Prepare each one as a first-person STAR story with a measurable result, and keep the energy answer specific to software work at a utility.

1. Discuss a project where you implemented a significant software feature. What challenges did you face?

Describe a time when you had to work under pressure. How did you handle it?

Medium · Behavioral / Leadership

Approach

1. Use STAR. Make the pressure concrete: a fixed date, a production incident or a regulatory deadline, and what would have happened if it slipped.
2. Spend the Action part on decisions: what you cut or deferred, who you told and when, and how you split or sequenced the work. Show judgement, not just longer hours.
3. Give a measurable result and what you changed afterwards so the same crunch did not happen again.
4. Avoid stories where all-nighters are the point, or where the pressure came from a mistake you do not own.

Follow-up

• What did you choose not to do, and who agreed to that?
• What would you change so it does not happen again?
• How did the pressure affect the rest of the team?

Give an example of how you contributed to a team project. What was your role?

Medium · Behavioral / Leadership

Approach

1. State the team's goal briefly, then make your role explicit: what you owned, what others owned, and where the two met.
2. Describe one or two specific contributions in the first person (a component you built, a blocker you removed, a review or test practice you introduced) and the reasoning behind them.
3. Show how you worked with others: handoffs, a dependency you negotiated, or helping a teammate. Cross-functional work with product, operations or other engineering teams fits this role well.
4. End with the outcome and your share of it. The mistake that sinks this answer is saying 'we' throughout so your role disappears.

Follow-up

• What would the project have looked like without your part?
• Where did you disagree with the team's approach?
• What did you learn from a teammate on that project?

How do you prioritize tasks when managing multiple deadlines?

Medium · Behavioral / Leadership

Approach

1. Name your method: rank by impact and urgency, check the dependencies (what blocks other people), and estimate the cost of each delay. Then show it on a real week where deadlines collided.
2. Show that you bring trade-offs to the people affected instead of quietly missing a date: who you told, what options you gave them, and what was renegotiated.
3. Mention the mechanics you use in an Agile or Scrum setup, such as backlog ordering, sprint planning, and breaking work into smaller pieces that can ship.
4. Avoid a generic to-do-list answer with no example, or saying that everything got done on time with no trade-off at all.

Follow-up

• Two stakeholders each say their item is top priority. What do you do?
• Tell me about a time you got the priority wrong.
• How do you handle urgent interruptions during a sprint?

Tell me about a conflict you had with a team member and how you resolved it.

Medium · Behavioral / Leadership

Approach

1. Choose a real disagreement, ideally a technical one (a design choice, a code review dispute, scope), where both sides had a reasonable position.
2. State what each person wanted and why, fairly, without making the other person the villain.
3. Show how it was resolved: a one-on-one conversation, evidence such as a benchmark, prototype or data, an agreed decision-maker, or a compromise. Make clear which step you took yourself.
4. Give the outcome and how the working relationship went afterwards. Stories where the conflict is trivial, or where you simply won, usually fall flat.

Follow-up

• What would you do if the evidence had gone against you?
• How did you work with that person afterwards?
• When do you escalate a disagreement instead of resolving it yourself?

What motivates you to work in the energy sector?

Medium · Behavioral / Leadership

Approach

1. Give a specific, honest reason tied to software work: reliability for customers who depend on the service, planning and operating the grid, or the challenge of building software in a regulated industry where mistakes have physical consequences.
2. Show you know what the company does: it delivers energy and utility services to customers in the United States, and transmission planning is one area the role touches. Do not repeat its marketing language back.
3. Connect your motivation to your experience: data-heavy systems, reliability work, compliance, or any utility or industrial project. If you have no energy background, say what you have done to learn about the sector.
4. Avoid a vague answer about sustainability, or one that would work equally well for any employer.

Follow-up

• How do you think software work at a utility differs from a tech company?
• Which part of the energy business interests you most, and why?
• How would you handle slower, regulated release cycles?