Data Analyst Job Description

Key skills

Responsibilities

• • Build and maintain dashboards and reports that give stakeholders reliable, self-service access to data
• • Define, document, and maintain a consistent set of business metrics and KPI definitions
• • Conduct ad hoc analyses to answer specific business questions quickly and accurately
• • Identify data quality issues, diagnose root causes, and coordinate fixes with data engineering
• • Partner with product, marketing, and operations teams to instrument new features and initiatives
• • Present analysis findings to stakeholders with clear narratives and actionable recommendations
• • Document data models, calculation logic, and analytical methodologies in a shared knowledge base
• • Proactively surface trends, anomalies, and opportunities that stakeholders may not have thought to ask about

Requirements

• • 2+ years of analytical work with a strong SQL track record on real business datasets
• • Demonstrated ability to build reliable dashboards in a mainstream BI tool
• • Experience defining metrics and KPIs that stakeholders trust and actively use
• • Strong communication skills — can explain a chart, its caveats, and the recommended action in plain language
• • Attention to detail and a strong instinct for finding and correcting data quality problems
• • Business acumen to prioritize analyses that have meaningful decision impact

Nice to have

• • Experience with dbt for transforming data in the warehouse
• • Familiarity with product analytics tools such as Amplitude, Mixpanel, or Heap
• • Basic Python or R for analyses that exceed spreadsheet or SQL capabilities
• • Experience setting up data instrumentation and event tracking for new product features
• • Experience defining a canonical metric or building a single source of truth that reduced conflicting numbers across teams
• • A portfolio of analyses where the recommendation, not just the chart, changed how a team operated

What to look for in a great Data Analyst

The best data analysts combine SQL craft with narrative skill — they don't just write correct queries, they package findings into stories that drive decisions. In interviews, listen for business curiosity: do they describe analyses in terms of what decision was made, or just what query they wrote? Look for data skepticism: strong analysts verify their numbers, check denominator definitions, and flag when a metric is misleading. A high quality bar for dashboards — thoughtful axis labels, well-chosen chart types, clear titles — signals that they think about the consumer of their work, not just the data itself.

Interview questions to ask a Data Analyst

Ask candidates to write a SQL query that requires window functions or a multi-level CTE to solve — observe their approach and how they handle edge cases. Present a mock dashboard with a confusing or misleading chart and ask them to critique and redesign it. Give a scenario: 'The sales team says revenue is up 20% this quarter but the product team says activation is down — how would you investigate?' This tests their ability to reconcile conflicting signals and think about metric definitions. Finally, ask about a time they caught a data quality issue and what they did about it — this surfaces analytical rigor and follow-through.

Where to source Data Analysts

Analyst-specific job boards and LinkedIn are reliable starting points. SQL-focused communities and data analytics forums surface practitioners who engage with the craft beyond their day jobs. Finance and consulting backgrounds are worth considering — former financial analysts and management consultants often bring strong business framing and quantitative rigor that transfers well. dbt community forums and Slack workspaces surface analysts who have invested in modern data stack skills. Look for public work: a Substack data newsletter, a public Tableau workbook, or a GitHub repo with polished analysis notebooks all signal genuine enthusiasm and communication skill.

Structuring the Data Analyst interview loop

Build the loop around the two things analysts actually do: get trustworthy numbers, and make them mean something. A strong sequence is a screening call, a practical SQL and data exercise on a realistic messy dataset, and a round where they present a finding and defend how they'd communicate it to a non-technical stakeholder. In the SQL round watch for how they handle joins that fan out, define a metric precisely, and sanity-check their own results rather than trusting the first number. In the storytelling round see whether they surface the 'so what' or just describe charts. Ask how they'd respond when a stakeholder wants the data to say something it doesn't. That blend of technical accuracy and clear, honest communication is what separates a reporting button-pusher from a genuine analyst.

Junior vs senior Data Analyst: what changes

A junior data analyst answers well-defined questions and builds dashboards to spec, usually with the metrics already agreed. A senior helps decide which questions matter, defines metrics so the whole company measures them the same way, and pushes back when a request is framed to confirm a preconception. Seniors catch data-quality problems before they mislead, design KPI frameworks, and turn a vague business worry into an analysis someone can act on. They also mentor on SQL craft and on honest interpretation. The step up is less about advanced techniques and more about trust: owning metric definitions, spotting when the data is wrong, and communicating findings so leaders make better decisions rather than just prettier reports.