Drillinginfo Software Engineer Interview: Questions, Experience & Prep (2026)

Drillinginfo, operating today as Enverus, builds data and analytics software for the energy sector, helping oil, gas, and power professionals make decisions using large volumes of production and geospatial data. Their engineering teams work on products that ingest, process, and serve this data at scale.

As of mid-2026, Drillinginfo has 38 open Software Engineer roles, making this an active hiring window. Candidates report the interview process typically involves a recruiter screening call, one or more technical rounds covering coding and system design, and a final round with senior engineers or an engineering manager. The process is structured and technically rigorous, with a strong emphasis on database performance, system design, and end-to-end ownership.

For context on compensation, the broader India market for Software Engineers shows the following ranges based on knok jobradar data:

These are market-wide ranges. Verify specific Drillinginfo figures directly with your recruiter.

Based on candidate reports and the nature of Drillinginfo's products, here are the questions that come up most often in their Software Engineer interviews:

1. Walk me through how you would design a data pipeline for large-volume time-series energy data.
2. How have you optimized a slow database query in production? What was your approach?
3. Describe a time you worked with a legacy codebase. What changes did you make and how did you manage risk?
4. How do you ensure reliability and correctness in a distributed system?
5. Tell me about a time you had to learn a new technology quickly to complete a project.
6. How would you design a REST API to expose well production data to third-party clients?
7. Describe how you have handled changing requirements mid-project.
8. What strategies do you use for writing maintainable and testable code?
9. How have you handled on-call duties or a production incident in a previous role?
10. Drillinginfo processes geospatial and time-series data. How would you approach indexing strategies for such datasets?
11. Tell me about a disagreement you had with a teammate or manager and how you resolved it.
12. How do you approach performance and load testing for a service you have built?

Q: How have you optimized a slow database query in production?

*Situation:* At my previous company, a key reporting page was loading slowly for users, causing repeated complaints from the business team.

*Task:* I was asked to investigate and resolve the issue without causing any downtime.

*Action:* I profiled the query using the database's explain plan, identified a missing composite index, and rewrote a correlated subquery as a join. I tested the change thoroughly in a staging environment and deployed during a low-traffic window.

*Result:* Page load time dropped noticeably, user complaints stopped, and overall database CPU usage fell as well. I documented the approach so the team could apply the same method to similar queries going forward.

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Q: Describe a time you worked with a legacy codebase.

*Situation:* I joined a team that maintained a monolithic service written several years earlier with almost no internal documentation.

*Task:* I needed to add a new feature without breaking existing functionality that multiple downstream services depended on.

*Action:* I read through the relevant modules carefully, wrote characterization tests to capture the existing behavior, then made targeted changes. I also improved the documentation as I worked so the next engineer would have an easier time.

*Result:* The feature shipped on schedule. The characterization tests I added caught a regression in a later release, saving the team hours of debugging that would otherwise have reached production.

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Q: Describe how you handled a situation where requirements changed mid-project.

*Situation:* Midway through building a data integration feature, the product team came back with a revised requirement that changed the data format the system needed to accept.

*Task:* I had to update the design and implementation without delaying the overall delivery by more than a day or two.

*Action:* I mapped the impact across all affected components, flagged the timeline risk to the product manager early, and re-prioritized the work with my team lead so we could absorb the change without dropping other commitments.

*Result:* We shipped a revised version within the same sprint with only a small delay. The client was satisfied and the integration ran cleanly in production from day one.

Use STAR for every behavioral question. Drillinginfo interviewers respond well to a clear Situation, Task, Action, Result structure. Keep the Situation and Task brief. Spend most of your time on the Action, describing specifically what you did. Always close with a concrete Result: did the bug get fixed, did the project ship, did adoption improve?

For system design, clarify before you draw. Candidates report that Drillinginfo interviewers expect you to ask about scale, constraints, and use cases before sketching any architecture. Ask about expected data volume, read-to-write ratio, latency requirements, and whether the solution needs to be real-time or batch. Then walk through your components one at a time.

For coding rounds, narrate your thinking. Think out loud. Interviewers want to see how you reason, not just whether you arrive at the right answer. State your assumptions, discuss trade-offs, and flag edge cases even when you do not have time to handle all of them.

For domain questions, connect to energy data. If you have worked with time-series databases, geospatial queries, or high-volume data pipelines, bring those examples in. If you have not, describe how you would approach those problems and show genuine curiosity about why the domain is interesting.

Strong database and data fundamentals. Drillinginfo's products are data-heavy and performance-sensitive. Interviewers typically look for a solid grasp of query optimization, indexing strategies, and efficient data structures. Surface-level answers tend not to go far.

Domain curiosity. Candidates who show interest in the energy sector, even without direct experience, tend to stand out. Spending time to understand what Enverus does at a product level signals that you are genuinely interested, not just mass-applying to every opening.

Ownership mindset. Candidates report that Drillinginfo values engineers who take end-to-end ownership: writing code, testing it, deploying it, and monitoring it in production. If you have on-call experience, production incident stories, or examples of improving observability, bring those forward.

Clear communication. Because Drillinginfo teams collaborate across locations and with non-technical stakeholders, interviewers pay attention to how clearly you explain your thinking. Practice describing complex technical decisions in plain language.

Collaborative problem-solving. Interviewers are also assessing whether you would be easy to work with. Be receptive to hints, ask clarifying questions, and engage with follow-ups rather than defending your first answer stubbornly.

Week 1: Foundations and company research. Read up on Enverus and the problems they solve for energy companies. Revisit core concepts: data structures, algorithms, database design, and distributed systems. Review SQL query optimization and indexing strategies, since these come up frequently in Drillinginfo rounds.

Week 2: System design practice. Practice designing data-heavy systems out loud. Focus on pipelines, APIs, and time-series storage. Use a 'requirements first' approach: clarify scale and constraints before sketching a solution. Review caching, message queues, and database sharding.

Week 3: Behavioral preparation. Write STAR stories covering: a technical challenge you solved, a time you dealt with legacy code, a conflict with a teammate, a project where requirements changed, and a time you improved system reliability. Practice saying these out loud, not just writing them down.

Week 4: Mock interviews and review. Do at least 2-3 mock interviews with a peer or a practice platform. Record yourself if possible. Review weak areas and refine your answers. The day before your interview, reread your STAR stories and check for any recent Enverus product news.

Coding practice. Work through medium-difficulty problems on LeetCode or a similar platform, focusing on arrays, strings, trees, graphs, and dynamic programming.

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Skipping requirements in system design. Candidates who jump straight into drawing architecture diagrams without clarifying requirements often build the wrong system. Always spend the first few minutes asking about scale, constraints, and use cases.

Generic behavioral answers. Saying 'I am a team player' or 'I learn quickly' without a concrete story wastes your interview time. Every behavioral answer needs a specific Situation and a clear Result.

Ignoring the domain. Some candidates treat Drillinginfo like any generic software company. Interviewers notice when you have not read anything about Enverus or the energy data space. A small amount of research goes a long way.

Not asking clarifying questions in coding rounds. Many coding problems have ambiguities. Drillinginfo interviewers typically expect you to identify these before writing code. Assuming all inputs are valid or that edge cases do not matter can cost you marks even if your core logic is correct.

Weak closing in behavioral answers. Many candidates describe what they did in detail but forget to state what actually happened as a result. Always close with the outcome: did the project ship on time? Did the team adopt the approach? Did the client renew? A clear result makes the answer feel complete.

Over-engineering in system design. Candidates sometimes propose extremely complex architectures when a simpler solution would work. Drillinginfo engineers want practical, maintainable systems. Show that you can reason about trade-offs and choose simplicity when it fits.