Data, Analytics & AI

Skill areas

Data & Analytics

Data Quality Analyst

A Data Quality Analyst ensures that the data an organisation relies on for decisions, reporting, and operations is accurate, complete, consistent, and fit for purpose. Day-to-day work involves profiling datasets to identify anomalies and errors, designing and running data quality checks and validation rules, investigating the root causes of data issues, working with data engineers and business teams to remediate problems, and maintaining data quality metrics dashboards. The role sits at a critical juncture between data engineering, data governance, and business operations — data quality issues that go undetected cost organisations in bad decisions, regulatory penalties, and lost customer trust. Entry-level positions typically focus on running existing quality checks, investigating flagged issues, and documenting findings. As analysts develop, they take on more of the rule design, root cause analysis, and stakeholder engagement involved in building a proactive data quality programme. In the UK, demand has grown significantly as GDPR requirements, regulatory reporting obligations, and the expansion of data-driven decision-making have made poor data quality a material business risk. Analysts with SQL proficiency and an understanding of data pipelines are the most sought-after at entry level.

Career information

Everything you need to know about this role

Salary, eligibility, background, qualifications, and where people typically work.

Expected salary range

£28,000–£45,000 (entry to mid-level, UK). Senior Data Quality Analysts £45,000–£60,000. Data Quality Managers and Heads of Data Governance £60,000–£85,000+. Financial services and healthcare roles in London typically sit at the higher end of each band.

Eligibility limits

Right to work in the UK required. Roles in financial services may involve FCA-related background screening given access to sensitive data. Healthcare roles may require DBS checks. No formal licence or registration required for commercial roles. Roles involving personal data require familiarity with GDPR obligations — some employers treat this as an eligibility criterion at interview.

Understand eligibility rules →

History and context

Data quality as a formal discipline emerged alongside the growth of enterprise data warehouses in the 1990s, when organisations first began consolidating data from multiple source systems and discovering the inconsistencies that resulted. The problem became more acute with the explosion of digital data sources in the 2000s, and the introduction of GDPR in 2018 made data accuracy and completeness a legal obligation for UK firms handling personal data — with the ICO empowered to issue substantial fines for data that is inaccurate or processed on an incorrect basis. Today, data quality is evolving rapidly. The growth of the modern data stack — with data ingested from dozens of sources through automated pipelines into cloud warehouses — has created new failure points and made manual quality checking unscalable. Tools like dbt tests, Great Expectations, and Monte Carlo have emerged to automate quality validation within pipelines. Data quality analysts are increasingly working at the intersection of data engineering and governance, designing automated checks and monitoring for anomalies rather than reviewing data manually. Organisations that treat data quality as a continuous engineering discipline rather than a periodic audit function are pulling ahead.

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History and context

Question 3 · #3

ScenarioMedium

Walk me through how you would investigate a data quality issue reported by a business user.

scenariodata-quality-analyst

Why they ask

Investigation is a core skill. Interviewers want to see a methodical approach — root cause analysis rather than symptom-fixing — and clear communication with the reporter.

What they want

A structured process from initial triage through root cause identification to resolution and prevention — not just "I would look at the data."

How to structure your answer

Understand the issue — what the user observed and what they expected
Reproduce the issue — find the specific records affected
Trace the lineage — follow the data back through the pipeline to find where it went wrong
Identify the root cause — source system, transformation logic, or mapping error
Remediate and prevent recurrence

Sample answer

My first step is to understand exactly what the user observed — what value they saw, what they expected to see, and in which report or system. I then try to reproduce the issue by finding the specific records in question and confirming the discrepancy is real rather than a display issue. Once confirmed, I trace the data lineage: where does this data originate, what transformations does it pass through, and at which point does the value become wrong? This usually means comparing the source system record against the data warehouse value and checking each transformation step in between. Root causes typically fall into a few categories: the source system is wrong, a transformation is applying incorrect logic, there is a mapping error between systems, or there is a timing issue — the data was correct when loaded but has since been updated at source without the change propagating. Once I have identified the root cause, I fix it at the right level — not just the symptom in the final table — and design a quality check to detect the same issue automatically in future so the next occurrence is flagged before a user reports it.

Common mistakes

Fixing the symptom in the final table without identifying the root cause — it will recur
Not reproducing the issue before starting the investigation — you risk investigating the wrong thing
Forgetting to communicate back to the user who reported the issue — they need to know it is resolved

Bonus tips

Mention data lineage tools (dbt docs, Collibra) as useful for tracing issues in complex pipelines
Note that some issues are systemic — a single investigation can uncover a class of problems affecting many records
Show awareness that root cause analysis sometimes involves source systems that the data team does not own — communication with upstream teams matters

Data Quality Analyst

Everything you need to know about this role

Expected salary range

Eligibility limits

History and context

Everything you need to know about this role

Expected salary range

Eligibility limits

History and context

Typical qualifications

Major employers

Where this role can take you

Overview

Progression opportunities

Typical timelines

Why this path matters now

Sample career paths

Data governance track

Related roles in this library

Career tips

Practise the questions you'll actually get asked

Tell me about yourself and why you are interested in data quality.

What are the key dimensions of data quality?

Walk me through how you would investigate a data quality issue reported by a business user.

How would you write a SQL query to identify duplicate customer records?

What is data lineage and why does it matter for data quality?

How would you communicate a data quality issue to a senior stakeholder who relies on the affected data?

Why should we hire you for this Data Quality Analyst role?

Data engineering / observability track