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Data analyst resume examples for graduate, product and senior roles

Browse four fictional data analyst resumes for entry-level (graduate), product analytics, healthcare and senior customer-insights roles. See SQL, Python, dashboard and data-quality work in context, then download a free PDF or editable Word (DOCX) example to adapt with your own facts.

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Entry-level data analyst (graduate)Graduate · Reporting and insight

Samira Wilson

Graduate Data Analyst | SQL, Python & Power BI

Adelaide, SA+61 491 570 737[email protected]linkedin.com/in/samira-wilson

Summary

Business analytics graduate from Flinders University with a summer internship in freight operations reporting and a capstone study of first-year student retention. Uses SQL and Python to clean and reconcile data, and builds Power BI reports that operations staff can read at a glance.

Skills

Analysis
SQL, Python (pandas), Excel, Descriptive statistics, Data cleaning
Reporting
Power BI, Tableau, Dashboard design, Written summaries
Practice
Data validation, Documentation, Stakeholder interviews, Presenting findings
Certification
Microsoft Certified: Power BI Data Analyst Associate (2025)

Education

Bachelor of BusinessFeb 2022 – Nov 2025
  • GPA 6.2 / 7
  • Topics: Statistics for Business, Database Systems, Predictive Analytics, Data Visualisation

Experience

Data Analyst InternNov 2024 – Feb 2025

Summer internship with the operations reporting team of a regional freight carrier.

  • Reconciled 18 months of delivery records from two dispatch systems, removing 12,400 duplicate rows.
  • Built a daily Power BI report of late deliveries and route exceptions for operations managers.
  • Found 9% of late deliveries logged against the wrong depot and helped dispatch fix the default.
  • Documented the 20 report fields and presented weekly findings to the operations and finance leads.

Tools: SQL, Python, Power BI, Excel

Projects

Student retention analysisMar 2025 – Oct 2025

Anonymised engagement data, analysed for an academic panel studying first-year retention.

  • Built Python notebooks joining enrolment, attendance and learning-platform data for 3,200 students.
  • Described three patterns linked to withdrawal, with confidence intervals and a clear note that the data could not show cause.
  • Presented a Tableau dashboard and recommendations to a five-person academic panel.
Product data analystMid-level · Product analytics

Noah Grant

Product Data Analyst | Experimentation, SQL & Self-Service Analytics

Sydney, NSW+61 491 571 266[email protected]linkedin.com/in/noah-grant

Summary

Product data analyst with six years of experience in consumer apps, working with product teams on activation, retention and experiment analysis. Builds tested dbt models and Looker explores so squads can answer routine questions themselves, and writes readouts that state the effect, the uncertainty and the decision it supports.

Skills

Analytics
SQL, Python, Experiment analysis, Cohort and retention analysis, Metric design
Data tools
BigQuery, dbt, Looker, Segment, Git
Communication
Experiment readouts, Data storytelling, Documentation, Workshop facilitation

Experience

Product Data AnalystJul 2022 – Present

Savings and budgeting app with about 450,000 monthly active users; embedded with four product squads.

  • Defined the activation and 30-day retention metrics used by four product squads and the leadership dashboard.
  • Analysed six onboarding experiments; the highest-performing treatment improved activation by 14% and was rolled out to all new users.
  • Added dbt tests to the product event models, which reduced dashboard defects reported by stakeholders by 36% over two quarters.
  • Estimated sample sizes and minimum detectable effects before each experiment launched, and stopped two tests that could not reach a useful result.
  • Audited 180 tracked events with engineering, removed 40 that nothing used and documented the rest in the event catalogue.
  • Ran Looker training for 30 product managers and designers, which moved routine funnel questions out of the analyst request queue.

Tools: SQL, Python, BigQuery, dbt, Looker, Segment

Reporting AnalystFeb 2021 – Jun 2022

Streaming and podcast app; reporting for the product, content and marketing teams.

  • Automated weekly engagement reporting with SQL and Python, cutting preparation from five hours to 45 minutes.
  • Created a shared metric glossary that resolved recurring differences between product and marketing reports.
  • Built content performance dashboards that the editorial team used to plan the weekly homepage.
  • Answered about ten ad-hoc questions a week from the content team and turned the recurring ones into saved reports.

Tools: SQL, Python, Looker, BigQuery

Graduate Data AnalystFeb 2020 – Jan 2021

Graduate role in the reporting team during the move to BigQuery.

  • Rebuilt the weekly subscriber report in SQL after the move to BigQuery, matching the old figures to within 0.5%.
  • Documented 25 reporting tables for the analyst onboarding guide.

Education

Bachelor of MathematicsFeb 2017 – Nov 2019
Healthcare data analystMid-level · Health service performance reporting

Ruby Collins

Healthcare Data Analyst | SQL, R & Power BI for Health Service Performance

Melbourne, VIC+61 491 572 665[email protected]linkedin.com/in/ruby-collins

Summary

Healthcare data analyst with six years of experience in public hospital and primary health reporting. Uses SQL, R and Power BI to turn patient flow, waitlist and service activity data into reports that clinicians and managers use, and applies de-identification and small-cell suppression before any data leaves the organisation. Agrees indicator definitions with clinical leads so figures match across teams.

Skills

Analysis
SQL (SQL Server), R, Excel, Data linkage, Descriptive statistics
Reporting
Power BI, Performance indicators, Board reporting, Data definitions
Data governance
De-identification, Small-cell suppression, Data quality checks, Release logs

Experience

Data Analyst, Health Service PerformanceJul 2023 – Present

Public health service with three hospitals; performance and reporting team of six analysts.

  • Rebuilt the emergency department length-of-stay report in SQL Server and Power BI, giving nurse unit managers a daily view by triage category.
  • Reconciled elective surgery waitlist extracts against the state submission each month, resolving 140 mismatched records before the reporting deadline.
  • Automated the monthly board performance pack with R and SQL, cutting preparation from three days to one and removing manual copy-and-paste steps.
  • Analysed discharge timing across 52,000 inpatient episodes; the bed management team moved two daily discharge huddles earlier based on the findings.
  • Wrote definitions for 35 performance indicators with clinical leads, ending recurring disputes over how readmissions were counted.
  • Applied small-cell suppression and de-identification rules to every external report and logged each release in the data governance register.

Tools: SQL Server, R, Power BI, Excel

Reporting AnalystFeb 2021 – Jun 2023

Regional primary health network commissioning GP, mental health and after-hours services.

  • Built Power BI dashboards tracking referrals and wait times for 14 commissioned mental health services.
  • Linked de-identified GP practice data with service activity to report chronic disease screening rates by local government area for the planning team.
  • Cut quarterly contract reporting effort by about 30% by standardising provider data templates and the validation rules agreed with each provider.
  • Prepared the needs assessment tables and charts used in the regional three-year health plan.
  • Answered data requests from program managers and turned the recurring ones into self-service reports.

Tools: SQL, Power BI, Excel

Graduate Data OfficerMar 2020 – Jan 2021

Graduate role in the data and reporting team during a year of rapid service changes.

  • Cleaned and loaded weekly service activity files from 20 providers into the reporting database.
  • Maintained telehealth uptake trackers in Excel for the commissioning team's weekly planning meeting.

Education

Bachelor of Health SciencesFeb 2016 – Nov 2019
Senior customer insights data analystSenior · Customer reporting and data quality

Priya Shah

Senior Data Analyst | SQL, Python & Customer Insights

Melbourne, VIC+61 491 571 491[email protected]linkedin.com/in/priya-shah

Summary

Senior data analyst with eight years of experience in retail and marketplace analytics, specialising in customer reporting, SQL and Python automation, self-service dashboards and data quality controls. Explains results with enough context for commercial and product teams to act on them, and states the uncertainty and limits of each analysis.

Skills

Analysis
SQL, Python (pandas), Cohort analysis, Experiment analysis, Customer segmentation
Reporting
Looker, Tableau, Dashboard design, Self-service analytics, Metric definitions
Data quality
Validation checks (Great Expectations), Reconciliation, Source definitions, Documentation

Experience

Senior Data Analyst, Customer InsightsAug 2021 – Present

Homewares retailer with 38 stores and an online store; customer insights team of four analysts.

  • Automated the weekly customer performance pack with SQL and Python, cutting preparation from six hours to 40 minutes and removing two manual spreadsheet hand-offs.
  • Built a self-service retention dashboard in Looker used by 45 product, marketing and operations stakeholders; it replaced 11 recurring ad-hoc requests.
  • Added validation checks on customer segment tables, covering row counts, null rates and reconciliation to finance totals, which reduced recurring reporting defects by 31%.
  • Analysed two onboarding tests designed by the product team, reporting each effect with a confidence interval and a plain-English recommendation.
  • Mapped second-purchase timing across 2.3 million loyalty transactions; marketing now times its reactivation emails to the window the analysis found.
  • Mentor two analysts on SQL style, documentation and stakeholder write-ups.

Tools: SQL, Python, Looker, BigQuery, Great Expectations

Data AnalystFeb 2019 – Jul 2021

Online marketplace for regional producers; analyst for the commercial and seller operations teams.

  • Created customer cohorts in SQL that removed about 10 hours of manual segmentation work each week.
  • Standardised the recurring commercial reports and documented source definitions with finance and marketing.
  • Built a seller health report that flagged sellers with falling order volumes for the account team to contact.
  • Analysed delivery-fee changes in two regions and reported the effect on order frequency to the commercial lead.
  • Translated stakeholder questions into reproducible SQL analysis and one-page decision summaries.

Tools: SQL, Python, Tableau, PostgreSQL

Graduate AnalystFeb 2018 – Jan 2019

Graduate role in the commercial reporting team.

  • Maintained weekly sales and category reports in Excel and Tableau for the commercial team.
  • Moved three recurring reports from spreadsheets to scheduled SQL queries, removing about four hours of manual work a week.
  • Checked weekly figures against the finance ledger before release and logged each difference with its cause.

Education

Bachelor of CommerceFeb 2015 – Nov 2017

Certifications

  • Google Data Analytics Professional Certificate, 2020
Curated targeting walkthrough

One data analyst resume, two job descriptions

Both fictional jobs below use Priya Shah's senior customer insights data analyst resume. The source facts stay the same while the emphasis changes. These are curated writing examples, not outputs from the resume generator or comparative research.

Read the complete source resume →
Fictional job description A

Data Analyst — Reporting Automation

Automate weekly customer reporting with SQL and Python, maintain self-service Looker dashboards and develop production dbt transformation models.

What changes in emphasis

Lead with time saved and the stakeholders using the dashboard. The source supports reporting automation and adoption; it does not support every tool in the job brief.

Exact source evidence

Automated the weekly customer performance pack with SQL and Python, cutting preparation from six hours to 40 minutes and removing two manual spreadsheet hand-offs.

Fernvale Home — Senior Data Analyst, Customer Insights

Built a self-service retention dashboard in Looker used by 45 product, marketing and operations stakeholders; it replaced 11 recurring ad-hoc requests.

Fernvale Home — Senior Data Analyst, Customer Insights

Allowed tailored wording

Automated the weekly customer performance pack with SQL and Python, cutting preparation from six hours to 40 minutes. Built a self-service retention dashboard in Looker used by 45 product, marketing and operations stakeholders.

Requirements that stay unsupported

  • Production dbt modelling is not recorded in this source resume.
  • Dashboard delivery does not establish data-platform architecture ownership.
Fictional job description B

Data Analyst — Retention Insights

Analyse customer retention and onboarding tests, own experiment randomisation and sample-size choices, and attribute revenue changes to the retention programme.

What changes in emphasis

Lead with customer behaviour and the interpretation of tests. Keep the distinction between analysing product-team tests and designing those tests.

Exact source evidence

Mapped second-purchase timing across 2.3 million loyalty transactions; marketing now times its reactivation emails to the window the analysis found.

Fernvale Home — Senior Data Analyst, Customer Insights

Analysed two onboarding tests designed by the product team, reporting each effect with a confidence interval and a plain-English recommendation.

Fernvale Home — Senior Data Analyst, Customer Insights

Allowed tailored wording

Mapped second-purchase timing across 2.3 million loyalty transactions to inform reactivation timing. Analysed two onboarding tests designed by the product team, reporting each effect with a confidence interval.

Requirements that stay unsupported

  • Experiment randomisation and sample-size ownership are not shown; the tests were designed by the product team.
  • The source does not attribute a revenue change to the retention analysis or emails.
Resume writing tips

How to write a data analyst resume

Start with the decision your analysis supports, then show the tools, checks and communication behind it. These four fictional examples show what to emphasise at entry-level, product analyst, healthcare and senior levels.

Connect SQL and Python to useful work

Explain what you queried, automated or analysed and why it mattered. The senior example connects SQL and Python to a weekly reporting workflow with less preparation time and fewer manual hand-offs. A tool list alone does not explain the result.

Show who uses your dashboards

Name the audience, the question answered and the action supported. The graduate example reports delivery exceptions to operations managers; the senior example gives product, marketing and operations stakeholders a self-service retention dashboard.

Make data quality work visible

Describe the checks behind reliable numbers. The senior example uses row counts, null rates and reconciliation to finance totals; the product analyst adds tests to event models. Link validation work to fewer defects or clearer definitions when you can support that outcome.

Separate analysis from experiment ownership

Say whether you designed a test, estimated its sample size or analysed its results. The senior example analyses tests designed by the product team and reports confidence intervals. The product analyst also estimates sample sizes. Use the wording that fits your own role.

Choose evidence that fits your experience level

Graduates can use an internship or capstone to show data cleaning, analysis and communication. Experienced analysts can lead with repeatable workflows, metric definitions and stakeholder decisions. Include only tools you used: dbt belongs in the product example, while the senior example shows other validation controls.

Write a summary for the analyst role you want

Open with your level, the data you work with, your main tools and the kind of decision your work supports. The entry-level summary leads with a degree, an internship and a capstone; the healthcare summary names patient flow and waitlist data and its privacy controls; the senior summary names customer reporting and data quality. Keep it to two or three sentences and leave out tools the bullets below do not show.

Choose keywords you can prove

Data analyst job ads repeat SQL, Python or R, Excel, Power BI or Tableau, dashboards, data quality and stakeholder communication. List a keyword only when a bullet shows it in use. The healthcare example lists de-identification because one bullet applies it to every external report; none of the examples list machine learning because none of them did that work.

Apply the example to a job

Map the role before rewriting the resume

Compare the job's requirements with your own experience. Keep partial matches qualified and leave out requirements you cannot support.

Reporting automation

Matched

Automated a SQL and Python reporting workflow that cut weekly reporting from six hours to 40 minutes.

Dashboards

Matched

Built a self-service retention dashboard used by 45 product, marketing, and operations stakeholders.

Experiment analysis

Partial

Analysed two onboarding tests and presented confidence intervals, but experiment design ownership is not shown.

Data quality

Matched

Added validation checks that reduced recurring customer-segment reporting defects by 31%.

dbt

Gap

No production dbt ownership found. Keep the tool out unless a real transformation or testing example exists.

Before and after

Turn a generic bullet into role-relevant evidence

Original resume bullet

Created reports and dashboards for business stakeholders using company data.

Tailored from supported evidence

Automated weekly customer reporting with SQL and Python, reducing preparation time from six hours to 40 minutes and enabling self-service analysis for 45 stakeholders.

  • Replaces a generic claim with measurable time saved
  • Names supported tools and stakeholder reach
  • Keeps dbt out without evidence
Supported skills

Keep the skills list connected to evidence

SQLPythonDashboardingExperiment analysisData qualityStakeholder communication

These terms belong in the example because the evidence map supports them. A keyword from the job description is not enough on its own.

Role-specific review

Data Analyst resume checklist

  • Name the decision supported
  • Quantify time, defects, adoption, or impact
  • Separate analysis from experiment ownership
  • Show communication to non-technical teams
  • Do not add tools appearing only in the JD
Common questions

Data Analyst resume questions

What should a data analyst resume quantify?

Show time saved, quality improvements, adoption, reporting reach, decision impact, or another result the source evidence supports.

Should a data analyst list SQL and Python separately?

Yes when both were used in meaningful work. Connect each tool to an analysis, workflow, model, or outcome.

How should a data analyst describe dashboards?

State who used the dashboard, what decision it supported, and how adoption or reporting quality changed.

Should a data analyst claim experiment design experience?

Only if you designed or owned the experiment. Analysing results is valuable but different evidence.

Is dbt required on every data analyst resume?

No. Add dbt only when you have real modelling, testing, or maintenance experience.

How do I write a data analyst resume summary?

In two or three sentences, state your level, the data you work with, your main tools and the decisions your analysis supports. Compare the entry-level, product, healthcare and senior summaries on this page.

Which keywords should a data analyst resume include?

Use the skills the job ad asks for that your experience supports, usually SQL, Python or R, a BI tool such as Power BI or Tableau, data quality and stakeholder communication. Show each one in a bullet, not only in the skills list.

What should an entry-level data analyst resume show?

Lead with your degree, then an internship, capstone or project that shows data cleaning, analysis and a clear write-up for a named audience. The entry-level example uses one internship and one capstone.

Use your own career evidence

Tailor the facts, not the fiction

Compare a real job description with your own experience, keep unsupported gaps visible, and review every suggested claim before export.

Tailor my own resume

This fictional example demonstrates a review workflow. It does not guarantee ATS acceptance, interviews, or employment outcomes.