Data Analyst Job Description

A Data Analyst collects, cleans, and interprets data to help organizations make better decisions. They build reports, dashboards, and analyses that turn raw data into actionable insights. The role bridges raw data and business decisions — someone has to ask the right questions, find the data, and present it clearly.

What does a Data Analyst do?

A Data Analyst writes SQL queries to extract data from databases, cleans and transforms messy datasets, builds dashboards and reports in tools like Looker, Tableau, or Power BI, and presents findings to stakeholders. They partner with product, marketing, or operations teams to define metrics, track performance, and answer business questions. They also maintain data quality, document definitions, and ensure consistent reporting.

Data Analyst responsibilities

• Write SQL queries to extract and analyze data from relational databases
• Clean, transform, and validate datasets to ensure accuracy and consistency
• Build and maintain dashboards and reports using BI tools (Looker, Tableau, Power BI)
• Partner with stakeholders to define metrics, KPIs, and success criteria
• Perform ad-hoc analyses to answer specific business questions
• Present findings and recommendations to non-technical stakeholders
• Document data definitions, metrics calculations, and analysis methodologies
• Monitor data quality and flag inconsistencies or anomalies
• Support A/B testing and experimentation with statistical analysis
• Maintain data pipelines or coordinate with data engineering on pipeline reliability

Essential requirements

• Strong SQL skills for querying relational databases
• Experience with at least one BI tool (Looker, Tableau, Power BI, or similar)
• Proficiency in Excel or Google Sheets for data manipulation
• Ability to translate business questions into data queries and vice versa
• Clear communication skills for presenting data findings to non-technical audiences
• Basic understanding of statistics (averages, distributions, correlation vs. causation)

Preferred qualifications

• Experience with Python or R for data analysis and scripting
• Familiarity with data warehouse platforms (BigQuery, Snowflake, Redshift)
• Experience with A/B testing and experimental design
• Knowledge of the relevant industry's metrics and data landscape
• Experience with dbt or similar data transformation tools

Core skills

Technical / professional skills

• SQL (PostgreSQL, MySQL, BigQuery, Snowflake)
• BI and visualization tools (Looker, Tableau, Power BI, Metabase)
• Spreadsheets (Excel, Google Sheets) with pivot tables and formulas
• Programming for data analysis (Python with pandas, NumPy; or R)
• Data warehouse platforms (BigQuery, Snowflake, Redshift)
• Basic data modeling and transformation concepts
• Statistical analysis and A/B testing

Soft skills

• Curiosity — asking the right questions before diving into the data
• Attention to detail — catching data quality issues before they become bad decisions
• Communication — explaining findings in plain language, not just numbers
• Business acumen — understanding what the data means for the organization
• Time management — prioritizing analyses that have the highest business impact

Experience and education guidance

Entry-level Data Analyst roles typically require 0-2 years of experience or equivalent project/portfolio work. Mid-level roles expect 2-4 years with the ability to work independently on analyses. Senior roles require 4+ years and involve mentoring, complex analysis design, and cross-functional influence. The specific requirements depend on the complexity of the data environment and the business questions being asked.

Data Analysts commonly hold degrees in Statistics, Mathematics, Computer Science, Economics, or Business. However, many skilled analysts are self-taught or come through bootcamps and certification programs. Focus requirements on demonstrated SQL and analytical skills rather than specific degree requirements.

What to include in this job description

Include the specific tools and databases the role uses, the business teams the analyst partners with, whether the role is more reporting-focused or analysis-focused, and what kinds of questions the analyst is expected to answer. Vague descriptions attract candidates who can't do the actual work.

Common job description mistakes for this role

Common mistakes include requiring a specific degree when the work doesn't demand it, listing Python/R as required when the role is primarily SQL + BI, not specifying whether the role is more reporting or ad-hoc analysis, and describing the role as 'data science' when it's actually analysis.

How to customize this job description

After generating a Data Analyst job description, adjust the technical requirements to match the actual stack. If the role is primarily SQL + Tableau, don't require Python. If it involves statistical modeling, add that context. Specify the business domain (product analytics, marketing analytics, financial analytics) for better candidate targeting.

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Frequently asked questions