A Software Engineer at Corva (TX) plays a pivotal role in redefining how the energy industry interacts with real-time data. Unlike traditional oil and gas firms that treat software as an afterthought, Corva (TX) is built from the ground up as a high-growth technology company. In this role, you will design, build, and scale cloud-native applications and high-throughput data pipelines that process massive streams of real-time drilling and completions data. Your work directly impacts operational safety, efficiency, and sustainability on active drilling rigs around the world. As part of the engineering team, you will collaborate closely with product managers, UX designers, and domain specialists like Solutions Engineers and Field Engineers. The challenges you will tackle are highly complex, involving low-latency data processing, sophisticated data visualization, and predictive analytics. You will be building a platform that empowers users to make critical, split-second decisions based on live stream data, making your contributions highly visible and strategically vital to the company's rapid market expansion. You will be expected to write clean, maintainable code while maintaining a strong user-centric mindset. It is an exciting opportunity for engineers who want to solve real-world physical challenges through cutting-edge software engineering.
HR Phone Screen
reportedConversational call focused on your background, career goals, and cultural alignment.
What to demonstrate
- Conversational call focused on your background, career goals, and cultural alignment
- Depth in Coding Problem Solving
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 Screening
reportedAssessment of your resume and foundational engineering knowledge.
What to demonstrate
- Assessment of your resume and foundational engineering knowledge
- Depth in Coding Problem Solving
How to prepare
- Answer aloud and timed: How do you optimize database queries for time-series data when dealing with millions of new records daily?
- Answer aloud and timed: Describe your experience with microservices architecture. What are the primary trade-offs when moving from a monolith to microservices?
Technical Panel
reportedCore evaluation through technical discussions and live coding rounds.
What to demonstrate
- Core evaluation through technical discussions and live coding rounds
- Depth in Coding Problem Solving
How to prepare
- Answer aloud and timed: Walk me through how you would handle data loss or network latency issues in a distributed streaming environment.
- Answer aloud and timed: Implement a function to parse and aggregate streaming sensor data within a sliding time window.
Executive Leadership Conversation
reportedHigh-level discussion with executive leadership to ensure mutual fit.
What to demonstrate
- High-level discussion with executive leadership to ensure mutual fit
- Depth in Coding Problem Solving
How to prepare
- Answer aloud and timed: Write an algorithm to detect anomalous spikes in real-time drilling metrics based on historical averages.
- Answer aloud and timed: Given a dataset of drilling activities, how would you efficiently sort and filter the records based on multiple dynamic criteria?
PracHub editorial advice for the preparation topics above.
Emphasize your tech-first mindset
Remember that Corva (TX) views itself as a tech company disruptor in an industrial space. Highlight your experience with modern, cloud-native tech stacks and agile methodologies, and contrast this with slow-moving, legacy enterprise practices.
Practice collaborative coding
During the live programming round, do not code in silence. Treat the interviewers as your peers. Ask questions to clarify requirements, explain your architecture choices before writing code, and welcome their feedback.
During the live coding round, the interviewers are highly collaborative
Do not hesitate to ask questions if you get stuck; they actively look at how well you take feedback and hints.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a function to parse and aggregate streaming sensor data within a sliding time window.
Implement a function to parse and aggregate streaming sensor data within a sliding time window.
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?
Write an algorithm to detect anomalous spikes in real-time drilling metrics based on historical averages.
Write an algorithm to detect anomalous spikes in real-time drilling metrics based on historical averages.
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?
Given a dataset of drilling activities, how would you efficiently sort and filter the records based on multipl
Given a dataset of drilling activities, how would you efficiently sort and filter the records based on multiple dynamic criteria?
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?
Solve a classic string manipulation or array-based algorithmic problem, focusing on optimal time and space com
Solve a classic string manipulation or array-based algorithmic problem, focusing on optimal time and space 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?
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?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
How would you design a real-time data ingestion pipeline that handles high-frequency sensor data from thousand
How would you design a real-time data ingestion pipeline that handles high-frequency sensor data from thousands of IoT devices simultaneously?
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?
Explain the difference between WebSockets and HTTP polling, and describe a scenario where you would choose one
Explain the difference between WebSockets and HTTP polling, and describe a scenario where you would choose one over the other for real-time applications.
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 optimize database queries for time-series data when dealing with millions of new records daily?
How do you optimize database queries for time-series data when dealing with millions of new records 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?
Describe your experience with microservices architecture. What are the primary trade-offs when moving from a m
Describe your experience with microservices architecture. What are the primary trade-offs when moving from a monolith to microservices?
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 me through how you would handle data loss or network latency issues in a distributed streaming environmen
Walk me through how you would handle data loss or network latency issues in a distributed streaming environment.
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 me through the most technically challenging project on your resume. What were the bottlenecks, and how di
Walk me through the most technically challenging project on your resume. What were the bottlenecks, and how did you resolve them?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you approach writing automated tests for complex, data-heavy applications?
How do you approach writing automated tests for complex, data-heavy applications?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you balance technical debt with the pressure to deliver new features rapidly in a high-growth startup e
How do you balance technical debt with the pressure to deliver new features rapidly in a high-growth startup environment?
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?
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 Corva (TX) candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Corva (TX) loop
- Write out the reported sequence: HR Phone Screen, Technical Screening, Technical Panel, Executive Leadership Conversation.
- 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 Coding Problem Solving
- Spend the session on Coding Problem Solving, which Corva (TX) candidates report being tested on.
- Write one worked example in Coding Problem Solving and time yourself on it.
Deliverable: One timed worked example in Coding Problem Solving.
03Work Directional Drilling
- Spend the session on Directional Drilling, which Corva (TX) candidates report being tested on.
- Write one worked example in Directional Drilling and time yourself on it.
Deliverable: One timed worked example in Directional Drilling.
04Work Live Coding
- Spend the session on Live Coding, which Corva (TX) candidates report being tested on.
- Write one worked example in Live Coding and time yourself on it.
Deliverable: One timed worked example in Live Coding.
05Answer out loud: Technical & System Design
- Answer aloud, timed: How would you design a real-time data ingestion pipeline that handles high-frequency sensor data from thousands of IoT devices simultaneously?
- Answer aloud, timed: Explain the difference between WebSockets and HTTP polling, and describe a scenario where you would choose one over the other for real-time applications.
Deliverable: Spoken answers to 2 reported Technical & System Design question(s), under time.
06Answer out loud: Live Coding & Algorithms
- Answer aloud, timed: Implement a function to parse and aggregate streaming sensor data within a sliding time window.
- Answer aloud, timed: Write an algorithm to detect anomalous spikes in real-time drilling metrics based on historical averages.
Deliverable: Spoken answers to 2 reported Live Coding & Algorithms question(s), under time.
07Answer out loud: Experience & Resume Deep Dive
- Answer aloud, timed: Walk me through the most technically challenging project on your resume. What were the bottlenecks, and how did you resolve them?
- Answer aloud, timed: How do you approach writing automated tests for complex, data-heavy applications?
Deliverable: Spoken answers to 2 reported Experience & Resume Deep Dive 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.
Describe a time when you had to adopt a new technology or programming language quickly to deliver a project. W
Describe a time when you had to adopt a new technology or programming language quickly to deliver a project. What was your strategy?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a situation where you had a strong technical disagreement with a team lead or product manager. How di
Describe a situation where you had a strong technical disagreement with a team lead or product manager. How did you resolve 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?
Tell me about a time when a production deployment failed. What steps did you take to mitigate the issue, and w
Tell me about a time when a production deployment failed. What steps did you take to mitigate the issue, and what did you learn?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you handle working with ambiguous requirements on a fast-moving project?
How do you handle working with ambiguous requirements on a fast-moving project?
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?
Why do you want to transition your software engineering skills into the energy technology sector with Corva (T
Why do you want to transition your software engineering skills into the energy technology sector with Corva (TX)?
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
Describe a time when you had to adopt a new technology or programming language quickly to deliver a project. What was your strategy?
- 02
Describe a situation where you had a strong technical disagreement with a team lead or product manager. How did you resolve it?
- 03
Tell me about a time when a production deployment failed. What steps did you take to mitigate the issue, and what did you learn?
- 04
How do you handle working with ambiguous requirements on a fast-moving project?
How difficult is the technical interview at Corva (TX)?
Candidates generally rate the technical difficulty as average to slightly challenging. The coding questions are practical and focused on real-world scenarios rather than abstract academic puzzles. The panel round can feel intimidating due to the number of managers present, but the overall atmosphere remains supportive and collaborative.
Corva (TX) Software Engineer candidate reports ↗Do I need prior experience in the oil and gas industry to apply?
No. Corva (TX) is first and foremost a technology company. While familiarity with energy sector concepts is helpful, they prioritize strong software engineering fundamentals, system design capabilities, and a willingness to learn the domain over pre-existing industry experience.
Corva (TX) Software Engineer candidate reports ↗What is the final round with the executive team or CEO like?
The final round with the executive team or CEO is primarily focused on high-level cultural alignment, your long-term career goals, and your understanding of Corva (TX)'s business trajectory. While it can sometimes feel formal or slightly awkward, it is designed to ensure that you are aligned with the company’s vision and startup mindset.
Corva (TX) Software Engineer candidate reports ↗How fast does the hiring team make decisions?
The hiring process at Corva (TX) is exceptionally fast. Candidates frequently report receiving feedback or next-step scheduling within 24 hours of completing an interview round, with official offers often extended shortly after the final executive conversation.
Corva (TX) Software Engineer candidate reports ↗What topics does Corva (TX) test in interviews?
Corva (TX) interviews most often cover Solutions Engineering, Stakeholder Communication, Interview Process Management, Data Visualization, and Cross-Functional Collaboration. The exact emphasis depends on the specific role you apply for.
Corva (TX) 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.
- 01Corva (TX) 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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