Barclays Data Analytics Interview Questions and Answers (2026 Guide)

Quick answer: Barclays runs one of its largest technology and operations hubs outside the UK across Pune, Chennai, Noida, Mumbai and Gurugram, doing risk analytics, fraud and financial-crime surveillance, and quant model work rather than generic dashboards. Glassdoor aggregates rate its Data Scientist interview at 2 out of 5 difficulty (60% positive) and its Data Analyst interview at 3 out of 5 (56% positive), both citing SQL, Python and statistics themes. The single most detailed candidate account we found for a comparable analytics role, an on-campus Decision Analyst interview, covered SQL window functions, logic puzzles and probability rather than a data-science case study, and a separate values-based round tied to Barclays' RISES values is repeatedly confirmed across candidate forum threads.

About Barclays

Barclays is a British multinational bank, and its India centre, incorporated in 2007 as Barclays Global Service Centre, is one of its largest technology and operations hubs outside the UK, with offices in Pune, Chennai, Noida, Mumbai and Gurugram. One India-GCC-focused profile puts current headcount around 10,000 with a stated target of 11,500 by 2027, while the legal entity's own filings report a considerably larger figure, over 31,000 employees as of 2025, likely reflecting a broader scope than technology and analytics roles alone; we could not reconcile these two numbers to one authoritative count. (Business of GCC; The Company Check) What is consistent across sources is the type of work: the Pune campus runs trading-technology and low-latency systems work, and the centre builds quantitative risk models, regulatory capital calculation engines, and machine-learning models for credit-risk assessment, market-risk stress testing and financial-crime surveillance, alongside Barclaycard consumer-credit technology. (Business of GCC)

Barclays's Interview Process

• Data Scientist, Glassdoor aggregate (18 reviews): rated 2 out of 5 for difficulty with 60% of candidates reporting a positive experience, and an average of 37 days to get hired. Reported technical-screening themes include Barclays' data systems, SQL queries, statistical concepts like standard deviation, z-score and coefficients, machine-learning topics such as feature engineering and model-evaluation metrics, Python questions on pandas and mutable versus immutable objects, and a business case study for more senior roles. One candidate account dated February 2026 described 2 Python and 3 SQL questions in the first round. We could not load this Glassdoor page directly, so these details come from a search-indexed summary rather than a page read in full. (Glassdoor)

• Data Analyst, Glassdoor aggregate: rated 3 out of 5 for difficulty with 56% of candidates reporting a positive experience, below Barclays' own company-wide average of 62.4% positive. Reported topics span statistics, SQL, Python and Excel, with some candidates naming foundational questions like string palindromes and the classic second-highest-salary SQL query rather than exotic algorithms. (Glassdoor)

• Decision Analyst, on-campus India hiring (GeeksforGeeks candidate account, 2023): this is the single most detailed, individually attributable account we found for an analytics-adjacent Barclays role. A single technical round of 40-plus minutes with two interviewers covered a SQL query requiring the DENSE_RANK() window function, a follow-up on the difference between RANK() and DENSE_RANK(), a GROUP BY question, two logic puzzles (a modified "fastest three horses" problem and the mislabeled-jars puzzle), a probability question using permutations and combinations, and an extended project discussion. This candidate was not advanced to an HR round. (GeeksforGeeks)

• "Critical Skills, Values and Mindset" round: multiple independent Glassdoor Community forum threads from candidates interviewing for different roles describe a distinct round by this name, tied to Barclays' RISES values (Respect, Integrity, Service, Excellence, Stewardship), roughly 45 minutes, using situational and behavioural questions rather than direct "what does integrity mean to you" prompts. This is corroborated by more than one candidate thread but we found no single official Barclays document describing its exact format. (Glassdoor Community; Glassdoor Community)

What Candidates Report

Across the accounts above, three technical themes recur regardless of exact job title: SQL, especially window functions (RANK, DENSE_RANK) and GROUP BY logic rather than pure syntax recall; Python centred on pandas and core language behaviour like mutability; and statistics fundamentals (standard deviation, z-score, coefficients) paired with basic ML evaluation-metric knowledge for more senior Data Scientist openings. Business-context questions do surface, one on-campus Business Analyst candidate was asked how they would verify whether someone opening an account was a terrorist, which fits Barclays' compliance and financial-crime remit, but we found no analogous, sourced example of a fraud-analytics or credit-risk case study being asked specifically in a Data Scientist or Data Analyst loop.

What We Could Not Verify

We found no candidate-sourced, verbatim case-study question tied to Barclays' actual India GCC work, credit-risk modelling, market-risk stress testing or financial-crime surveillance, even though that work is confirmed to happen there. This is a plausible but unattested interview theme, not a confirmed one. Both core Glassdoor interview pages returned access errors on direct fetch, so those specific figures rest on search-indexed summaries rather than pages we read in full. We also could not resolve Barclays India's headcount to one authoritative number: sources range from about 10,000 to over 31,000 depending on which legal entity or scope is being counted, and we are not presenting either figure as more accurate than the other.

How to Prepare

• Drill SQL window functions specifically: RANK() versus DENSE_RANK(), plus GROUP BY logic, since this is the one topic independently confirmed by name in a detailed candidate account.

• Revise pandas and core Python behaviour such as mutable versus immutable objects, which shows up in the Data Scientist aggregate.

• Prepare one real story per RISES value (Respect, Integrity, Service, Excellence, Stewardship) for the Critical Skills, Values and Mindset round, since multiple candidates confirm this is evaluated through situational questions, not direct definitions.

• Refresh statistics fundamentals, standard deviation, z-score and coefficients, alongside basic ML evaluation metrics, if interviewing for a Data Scientist rather than Data Analyst title.