Data Analyst Interview Questions

1.1The data analyst role - what it delivers to the business and how it fits in different organisations

1.2Types of analytics - descriptive, diagnostic, predictive, and prescriptive and when each is used

1.3The analytics workflow - from business question to data to insight to decision

1.4Data literacy - working with stakeholders who do not understand data and helping them ask better questions

2.1SQL fundamentals - SELECT, WHERE, GROUP BY, JOIN, and writing queries that return accurate results

2.2Advanced SQL - window functions, CTEs, subqueries, and handling complex analytical queries

2.3Query optimisation - writing efficient SQL, understanding execution plans, and avoiding performance problems

2.4Working with large datasets - partitioning, indexing, and managing query cost on big data platforms

3.1Data quality - identifying missing values, duplicates, outliers, and inconsistencies

3.2Data transformation - reshaping, pivoting, merging, and preparing data for analysis

3.3Working with messy real-world data - what to do when the data does not match the documentation

3.4Data pipelines - how data gets from source systems to the analyst and what can go wrong

4.1Descriptive statistics - mean, median, standard deviation, and what they actually tell you

4.2Distributions - normal, skewed, bimodal, and how distribution shape affects analysis

4.3Correlation vs causation - one of the most important distinctions in data analysis

4.4Hypothesis testing - t-tests, chi-square, p-values, and when statistical significance matters

5.1Choosing the right chart - bar, line, scatter, heatmap, and when each communicates clearly

5.2Dashboard design - layout, hierarchy, and building dashboards that drive decisions

5.3Storytelling with data - building a narrative from analysis that gets people to act

5.4Common visualisation mistakes - misleading charts, chart junk, and how to avoid them

6.1BI tool fundamentals - working in whatever the business uses to connect data to reports

6.2Data modelling in BI - dimensions, measures, star schema, and building reliable report structures

6.3Self-service analytics - enabling business users to answer their own questions safely

6.4Report governance - version control, documentation, and ensuring reports are trusted and used

7.1Programmatic data analysis - working with data in code rather than manually in spreadsheets

7.2Data manipulation - filtering, aggregating, joining, and transforming data at scale

7.3Automation - writing scripts that run analyses automatically rather than manually repeating them

7.4Visualisation in code - creating publication-quality charts from data programmatically

8.1Translating business questions into analytical questions - the skill that separates great analysts

8.2Presenting analysis to non-technical stakeholders - what to include, what to leave out, and how to land the insight

8.3Managing analytical requests - scoping, prioritising, and pushing back on poorly defined questions

8.4Influencing decisions with data - what to do when your analysis gets ignored

9.1Data governance - definitions, ownership, lineage, and why it matters for analysts

9.2GDPR and data privacy - what analysts must know about personal data and how to handle it

9.3Data ethics - bias in data, fairness in analysis, and the responsibility analysts have

9.4Building trust in data - documentation, quality checks, and making sure people rely on your numbers

10.1Technical questions - SQL, statistics, Python, and analytical problem-solving under pressure

10.2Case study interviews - how to approach an open-ended business problem with data

10.3Behavioural questions - working with ambiguity, influencing stakeholders, and owning an insight that was wrong

10.4Salary negotiation, evaluating a data team, and questions that reveal data culture in an organisation