Fidelity Investments Data Engineer interview typically runs 3 rounds: two phone interviews and a final HR interview. It usually takes a few weeks and is relatively straightforward and user-friendly.
$121K
Avg. Base Comp
$177K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Fidelity is far more interested in whether you can explain the basics cleanly than in whether you can impress them with edge-case complexity. In the data engineering conversations we’ve seen, the standout signal was a simple but telling prompt around relational databases in SQL. That points to a team that values conceptual clarity over cleverness: if you can describe how tables relate, why a query behaves a certain way, and what the data model is doing, you’re already speaking their language.
A recurring theme is how approachable the process feels. Multiple candidates described the team as supportive and the interviews as straightforward, which usually means the bar is less about pressure and more about consistency. We’ve also seen that the company wants answers that are structured and easy to follow, especially when the conversation turns behavioral. The insistence on STAR format suggests they care about disciplined communication and whether you can turn past work into a clear, credible story rather than a vague summary.
For Fidelity specifically, the non-obvious thing that can make or break you is not depth for its own sake, but whether your explanations feel reliable and grounded. That fits a financial-services environment where trust matters as much as technical skill. Candidates who do well here tend to sound calm, precise, and practical — the kind of person who can make complex systems understandable without overcomplicating them.
Synthesized from 1 candidate report by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Fidelity Investments process.
I interviewed at Fidelity for a data engineer role, and it turned out to be a fairly comprehensive process spanning multiple rounds. The first technical round was with a senior manager who focused on foundational concepts—nothing too deep, just making sure I had the basics down. That went smoothly enough.
The second round was where things got more intense. I had a technical plus managerial interview with a lead and a manager, and they really dug into data modeling concepts, AWS, and Databricks specifics. This was noticeably harder than the first round; they weren't just checking knowledge but testing how I'd approach real problems and work within their stack. The questions weren't trick questions, but they required solid understanding rather than surface-level familiarity.
The final round was HR-focused—pretty standard stuff covering background, fit, and then salary negotiation. By that point I'd already gone through the technical gauntlet, so it felt like a formality. Overall, the process took a while; there were gaps between rounds where I didn't hear much, which made it hard to gauge momentum. The technical demands were pitched at a reasonable level for a data engineer role, not overwhelming but definitely substantive enough to separate candidates who really knew their stuff from those who didn't.
Prep tip from this candidate
Study data modeling principles thoroughly and be ready to discuss AWS services and Databricks architecture in detail—these came up repeatedly and formed the core of the technical assessment. The second round is the critical one; prepare for both technical depth and managerial questions about how you'd approach problems in a production environment.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Fidelity Investments
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Google Maps Improvement | |
| Fractional Shares | |
| Find the Index with Equal Left and Right Sum | |
| Hurdles In Data Projects | |
| Priority Queue Using Linked List | |
| Portfolio Platform Architecture | |
| RAG Strict Source Control | |
| Common Prefix | |
| Slow SQL Query | |
| Client Solution Pushback | |
| Seller Type Modeling | |
| Out of Stock Inventory | |
| Text Editor With OOP | |
| The Longest Journey | |
| Testing Constraints | |
| Reddit-like Notifications | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Subscription Overlap | |
| Comments Histogram | |
| Top Three Salaries | |
| Closest SAT Scores | |
| Last Transaction | |
| Find the Missing Number | |
| Cumulative Distribution | |
| Experiment Validity | |
| Paired Products |
Synthesized from candidate reports. Individual experiences may vary.
The first conversation is a technical phone screen focused on fundamentals rather than advanced coding. Candidates should be ready to explain core data engineering concepts clearly, including relational databases and basic SQL.
A second phone interview follows, again keeping the technical bar relatively straightforward. The discussion continues to emphasize foundational knowledge and clear communication of SQL and database concepts.
The last round is with two HR representatives and is entirely behavioral. Answers are expected in STAR format, with questions centered on past experiences, teamwork, and how candidates handle work situations.
