Data Analyst.

Yes. SQL is essential and should be listed prominently. Python or R proficiency increasingly separates competitive candidates from those limited to spreadsheets. Include specific libraries (pandas, matplotlib) and describe analyses where programming enabled insights that spreadsheet tools could not achieve at the required scale.

Describe the business context of your analyses: what question was asked, what stakeholder needed the answer, and what decision changed because of your work. Frame bullets around business outcomes rather than technical process. Analysts who understand the 'why' behind data requests are significantly more valuable than those focused only on technique.

Portfolios are highly valued for Data Analyst roles in creative, design, or technical fields. If your work is visual or project based, include a link to your portfolio in the resume header. Even if not required, a portfolio demonstrates initiative and gives hiring managers a deeper look at your capabilities.

A strong Data Analyst resume should include a clear professional summary, relevant work experience with quantified achievements, a skills section tailored to the job description, and education or certifications. Focus on outcomes and impact rather than listing responsibilities.

Use specific numbers, percentages, dollar amounts, or time frames to quantify your impact. For example, "increased revenue by 25%" or "managed a team of 12." Even if you do not have exact figures, provide reasonable estimates with context to demonstrate measurable results.