What to expect in Chevron data analyst interviews, based on three candidate reports: scenario-based data questions, behavioral stories, basic coding and data modeling, and a short recruiter-led process.
$108K
Avg. Base Comp
$125K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
The three candidate reports for Chevron analytics roles describe different processes, so prepare for a mix instead of one fixed format. The only offer came from a junior data analyst candidate, who describes a 15-minute recruiter call followed by a roughly 90-minute interview with a lead and a technical product owner. Questions were scenario-based: handling missing values, choosing visualization types for different situations, and naming the languages and tools they use. The interviewers pressed on surface-level answers, so be ready to explain why you would choose an approach.
That candidate also reports behavioral questions: where they saw themselves in five years, a problem they solved end to end including what went wrong, and how to explain technical work to non-technical stakeholders. The candidate received an offer about a week later.
Two candidates for Information and Analytics roles did not receive offers. One, in a phone interview, reports a single round with basic coding, an ER diagram built from a described scenario, and STAR-style behavioral questions. The other, an on-campus internship candidate, reports two rounds: a behavioral round, then a coding problem followed by a request to name five data structures for holding datasets and explain one in depth.
So prepare for missing-data handling, chart selection, ER diagrams, core data structures and STAR stories. Practice explaining each in plain language. With only three reports, the exact format may vary by team and level.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Chevron process.
I got contacted through social media after expressing interest in the role, and things moved pretty quickly from there. The process started with a brief 15-minute phone call where a recruiter walked through the core skills they were looking for and what the position would entail. It was informal and gave me a good sense of whether I wanted to move forward.
The main interview came next and lasted about an hour and a half. It was conducted in English with both a Lead and a technical Product Owner in the room, which initially felt a bit intimidating, but they were pretty approachable. The technical questions were scenario-based rather than pure algorithm problems. They asked me how I'd handle missing values in a dataset, what visualization types I'd choose for different situations, and which programming languages and tools I was comfortable with. Nothing felt like a gotcha, but I could tell they were listening closely—if I gave a surface-level answer, they'd dig deeper. That meant I really had to demonstrate that I actually understood the concepts, not just name-drop them.
The behavioral side was pretty substantial too. They threw in some classic questions like where I saw myself in five years, and they wanted me to walk through a specific problem I'd solved—how I approached it, what went wrong, and how I fixed it. They also asked how I'd explain technical things to stakeholders who don't have a programming background. That last one felt important for the role, since I'd be translating data insights for people across the organization.
The whole thing had a collaborative vibe. They weren't trying to stump me; they seemed genuinely interested in how I think through problems and communicate. I got an offer about a week later.
Prep tip from this candidate
Be ready with concrete examples of how you've handled messy data and how you'd explain your findings to non-technical audiences—they drill both of those hard. Also prepare a few specific problems you've solved end-to-end so you can walk them through your thought process without oversimplifying.
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Sourced from candidate reports and verified by our team.
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Synthesized from candidate reports. Individual experiences may vary.
The junior data analyst candidate reports a brief recruiter phone call covering the core skills and what the position would involve. It was informal. Another candidate was contacted through LinkedIn and phone before a telephone interview, so initial contact may differ by role.
The junior data analyst candidate describes an interview of about an hour and a half with a lead and a technical product owner. It combined scenario-based questions on missing values, visualization choices and tools with behavioral questions. Candidates may be pressed for depth if answers stay at surface level.
One phone-interview candidate reports basic coding questions and an ER diagram built from a described scenario. An on-campus internship candidate reports a coding problem followed by a data structures discussion. These topics may depend on the team and role.
Behavioral content appears in all three reports. One candidate was asked about a five-year plan, a problem solved end to end, and explaining technical ideas to non-programmers. Another reports STAR-format questions, and a third reports a dedicated behavioral round. Prepare concrete stories.
The junior data analyst candidate reports an offer about a week after the interview. The two Information and Analytics candidates report no offer. Because the reports cover different roles and formats, treat timing and next steps as variable.
