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Data Analytics

Overview

Data analysts convert raw, messy data into decisions. They work across every industry that generates data, which is all of them. The role requires a mix of SQL fluency, statistical thinking, tool proficiency, and the ability to communicate findings to people who did not ask technical questions. In smaller companies analysts own the full pipeline from extraction to boardroom slide. In larger organisations they specialise into product analytics, marketing analytics, or operations intelligence.

What does the Data Analytics role involve?

  1. Writing and optimising SQL queries against production databases or data warehouses.
  2. Cleaning and validating data quality before analysis.
  3. Building and maintaining dashboards in Power BI, Tableau, or Looker.
  4. Defining metrics in collaboration with product or operations stakeholders.
  5. Running ad-hoc analyses to answer specific business questions.
  6. Presenting findings to non-technical audiences and defending methodology under scrutiny.
  7. Documenting data sources and transformation logic for reproducibility.

Skills Required

  1. SQL, Excel/Sheets, data modelling basics, statistics intuition, BI tools (Power BI or Tableau), storytelling with data.

UK Salary Range

  • Entry level (0-2 years): £24,000 to £32,000 outside London. £28,000 to £40,000 in London. Reporting Analyst and BI Analyst roles at the lower end; Data Analyst roles at product-led companies at the higher end.

  • Mid-level (2-5 years): £38,000 to £55,000. Expectation of owning a data domain, building robust pipelines, and acting as an internal consultant for one or more business units.

  • Senior (5+ years): £55,000 to £75,000. Analytics Engineering and Data Lead roles at larger companies reach £80,000 to £100,000, particularly in London fintech.

  • Progression note: Python and dbt competence adds approximately £5,000 to £10,000 to market rate at mid-level. Analytics engineers (SQL-heavy, dbt-focused) are paid more than traditional BI analysts in most modern data teams.

UK Job Market

  1. UK demand is steady across retail, healthcare tech, fintech, and ops teams.
  2. Many roles are hybrid; SQL and a solid portfolio project are non-negotiable at junior level.

Who This Career Path Is For

  1. People who like structured thinking, attention to detail, and explaining numbers without jargon.

How to Get Started

Master SQL end-to-end, complete one end-to-end analytics project (extract, model, dashboard), learn stakeholder communication, then deepen with statistics or Python.

Deep guidance

Build Your Portfolio

What a strong analytics portfolio looks like

One end-to-end project is worth more than five dashboards with no explanation of the underlying analysis.

Project 1: End-to-end analytics case study

  • Choose a public dataset (Google BigQuery public datasets, Kaggle, or UK government open data).
  • Define a business question.
  • Extract, clean, and transform the data in SQL.
  • Build a dashboard in Power BI or Tableau.
  • Write a 500-word narrative explaining your methodology, key findings, and what you would recommend the hypothetical business do.
  • Publish the dashboard as a public link and host the SQL on GitHub.

Strong version: Includes data quality checks, explains what you filtered out and why, and names a specific business decision the analysis would inform.

Project 2: SQL skills showcase

  • Write five progressively complex SQL queries against a public dataset: a simple aggregation, a multi-table join with a WHERE clause, a window function for rolling averages, a cohort analysis, and a performance-optimised query with a query plan explanation.
  • Host on GitHub with output screenshots.

Project 3: A/B test analysis

  • Use a public A/B test dataset (there are several on Kaggle).
  • Calculate statistical significance, explain your interpretation, and write a one-page recommendation.
  • This demonstrates statistical thinking which many junior analysts lack.
How to Apply

CV tips

  • Lead with tools in context: not "proficient in SQL" but "wrote production SQL queries across a 50M-row Postgres database for a portfolio analytics project." Quantify wherever possible.
  • Hiring managers filter for evidence of impact, not just familiarity.

Where to look

  • LinkedIn with filters for Data Analyst and SQL.
  • Otta for scale-ups.
  • CityJobs for financial services.
  • NHS Digital and HMRC for public sector.
  • Many companies post analyst roles on their own careers pages without advertising on job boards.

Typical hiring process

  • Initial screen (30 minutes, mostly behavioural and CV walkthrough).
  • SQL technical test (take-home or live coding, 45 to 90 minutes).
  • Case study or presentation round where you walk through your analysis approach.
  • Final culture fit.
  • SQL tests are consistently the hardest filter at junior level: practise with Mode Analytics SQL Tutorial, StrataScratch, or LeetCode SQL.
Interview Preparation

SQL questions you must be ready for

  • "Write a query to find customers who made more than three purchases in the last 30 days." Test: GROUP BY, HAVING, DATE functions.
  • Show your approach before writing the query.
  • "How would you calculate month-over-month revenue growth?" Use LAG() window function or a self-join.
  • Explain both approaches and when you would choose each.
  • **"You have a table of orders.
  • Some customers have NULL values in the discount column.
  • How do you handle that?"** COALESCE or ISNULL.
  • Explain the business logic behind the imputation choice.

Analytical thinking questions

  • **"Conversion dropped 15 percent last week.
  • How do you investigate?"** Framework: confirm it is real (not a tracking issue), segment by device, channel, and region, check for coincident changes (deployments, campaigns), rule out data pipeline issues, form a hypothesis, test it.
  • "How would you set up a metric to measure the success of a new feature?" Define the north star metric, leading indicators, and guardrail metrics.
  • Agree on the measurement window and minimum detectable effect before launch.

Presentation round tips

  • Tell the story before the numbers.
  • Start with the business context and your hypothesis.
  • Show your methodology.
  • Lead with insight, not with tables.
  • Practice presenting to someone non-technical who will interrupt with questions.
Common Mistakes to Avoid

Mistake 1: Building dashboards that nobody asked for

  • A dashboard full of bar charts is not an analysis.
  • The output that impresses is a clear answer to a specific business question with evidence and a recommendation.

Mistake 2: SQL that works but is unreadable

  • Production SQL is read by colleagues.
  • Use CTEs instead of nested subqueries.
  • Name things clearly.
  • Add comments on complex transformations.
  • Poorly structured SQL in a portfolio signals that you have only solved problems for yourself, not for a team.

Mistake 3: Confusing correlation with causation

  • Junior analysts commonly make this mistake in interviews and case studies.
  • Always ask "what else changed at the same time?" before drawing causal conclusions.

Mistake 4: Ignoring data quality

  • Every dataset has problems.
  • Candidates who present analysis without mentioning null handling, outliers, or data source limitations appear inexperienced.
  • Mention what you checked and what you found.

Mistake 5: Not practising SQL under time pressure

  • SQL technical tests are timed.
  • If you can only write queries when you have unlimited time to look things up, you will fail live screens.
  • Practise writing correct SQL from memory.