Data Analyst Resume Example (+ Template)

Mid-level data analyst, around five years of experience. Two pages would be acceptable here, but this fits on one.

JORDAN REYES

Chicago, IL | (312) 555-0148 | jordan.reyes@email.com | linkedin.com/in/jordanreyes | github.com/jreyes-analytics

SUMMARY Data analyst with 5 years in retail and subscription businesses, working in SQL, Python and Tableau. Build and maintain reporting that around 200 users in merchandising, finance and marketing rely on, and run the analyses behind pricing and churn decisions. Comfortable owning a question end to end, from pulling the data to presenting the recommendation to directors.

EXPERIENCE

Senior Data Analyst, Midwest home goods retailer — Chicago, IL March 2022 – Present - Own the merchandising reporting layer in Tableau: 14 dashboards covering sales, inventory and returns, used by roughly 200 people across 9 regional teams. - Rebuilt the daily sales pipeline in dbt and Snowflake, cutting the morning refresh from 95 minutes to 22 and removing 3 manual steps that previously required an analyst to be online by 6 a.m. - Ran a pricing elasticity analysis across 1,200 SKUs in Python, which the merchandising director used to set the markdown calendar for two seasons. - Write and review SQL for a team of 4 analysts; set the review checklist and the naming standard now used across the analytics repo. - Present a monthly trading readout to 12 directors and VPs, translating a 40-page data pack into 5 slides and a recommendation.

Data Analyst, Subscription meal-kit company — Chicago, IL June 2020 – March 2022 - Built the churn model inputs and the cohort retention reporting used in weekly growth meetings, covering about 380,000 active subscribers. - Designed and analysed 18 A/B tests on onboarding and pricing pages, including sample size calculations and a written readout for each. - Replaced a 30-tab Excel forecast with a parameterised SQL model, reducing month-end close reporting from 2 days to 3 hours. - Partnered with customer support to define 9 standard contact reason codes, making it possible to tie support volume to product releases for the first time.

Reporting Analyst, Regional health insurer — Naperville, IL July 2019 – June 2020 - Produced 6 recurring regulatory and operational reports in SQL Server and Excel, on monthly and quarterly cycles, with zero missed deadlines. - Automated a 4-hour manual claims reconciliation in Power Query, bringing it under 20 minutes. - Documented 40+ field definitions in a shared data dictionary used by the reporting and actuarial teams.

SKILLS SQL (Snowflake, PostgreSQL, SQL Server) · Python (pandas, NumPy, scikit-learn, matplotlib) · dbt · Tableau · Power BI · Excel (Power Query, pivot tables, XLOOKUP) · Git · Airflow (basic) · A/B testing and sample sizing · Cohort and retention analysis · Data modelling (star schema) · Stakeholder presentation

EDUCATION B.S., Statistics — University of Illinois at Urbana-Champaign, 2019

CERTIFICATIONS Tableau Desktop Specialist (2021) · Google Data Analytics Professional Certificate (2020)

Entry-level variant. Same structure, different weighting: the summary states a direction, projects do the work experience usually does, and education sits higher on the page.

PRIYA NATARAJAN

Austin, TX | (512) 555-0193 | priya.natarajan@email.com | linkedin.com/in/priyanatarajan | github.com/pnat-data

SUMMARY Economics graduate moving into analytics, with SQL and Python from coursework and two analytics internships. Built a 4-table Postgres database and Tableau dashboard from public transit data as a capstone, and spent a summer cleaning and reporting on donor data for a nonprofit. Looking for a data analyst role where I can own recurring reporting and grow into deeper analysis.

EDUCATION B.A., Economics, minor in Statistics — University of Texas at Austin, May 2024 Relevant coursework: Econometrics, Database Systems, Statistical Inference, Data Visualization

PROJECTS - Transit reliability dashboard: Loaded 3 years of public on-time performance data (about 2.1 million rows) into PostgreSQL, modelled it into 4 tables, and built a Tableau dashboard showing delay patterns by route and hour. Code and write-up on GitHub. - Capstone: grocery price index: Scraped and cleaned 11 months of advertised prices in Python, then compared basket inflation across 5 store chains; presented findings to a class of 30 and a faculty panel.

EXPERIENCE

Analytics Intern, Regional nonprofit — Austin, TX June 2023 – August 2023 - Cleaned and deduplicated a donor file of 46,000 records, resolving about 3,800 duplicate households before the annual campaign. - Built 3 recurring Google Sheets reports on campaign response rates, handed over with a one-page instruction sheet. - Summarised giving patterns by channel for the development director in a 6-slide deck.

Student Data Assistant, University research centre — Austin, TX September 2022 – May 2023, 10 hours/week - Wrote SQL queries against a survey database of 120,000 responses to pull extracts for 5 faculty researchers. - Checked and corrected coding errors in 14 survey waves before analysis.

SKILLS SQL (PostgreSQL) · Python (pandas, matplotlib, BeautifulSoup) · Tableau · Excel (pivot tables, VLOOKUP, Power Query) · Google Sheets · R (coursework) · Git · Survey data cleaning

CERTIFICATIONS Google Data Analytics Professional Certificate (2024)

Work top to bottom. Each section has one job, and if it is not doing that job, cut it.

1. Header

Five items on two lines: name, city and state, phone, email, and one or two links. Include a LinkedIn URL and, if you have real code or dashboards to show, a GitHub or portfolio link. Leave out your street address — city and state is what employers need for location and tax questions.

If you are applying out of state, say so plainly: Chicago, IL (relocating to Denver, CO, June 2025). Hiring managers filter on location and will quietly skip a candidate they assume is 1,500 miles away.

Set your name at 16–20 points and the body at 10–11 points. Margins of 0.6–1.0 inch on all sides. One page if you have under about eight years of experience, two if you have more. Save and send as a PDF unless the posting asks for .docx.

2. Summary

Three or four lines, no heading flourish, no third person. Use this shape:

Summary formula

[Role or background] with [X] years in [industry or domain], working in [2–4 named tools]. [What you own or build, with scope — users, datasets, cadence]. [The kind of problem you want next, or the decision you influence].

Two worked examples:

Mid-level, healthcare

Data analyst with 6 years in hospital operations, working in SQL Server, Power BI and Python. Maintain the capacity and throughput reporting used daily by 11 department managers, and run the analyses behind staffing model changes. Strongest on messy operational data where the definitions have to be agreed before the numbers mean anything.

Career changer from accounting

Former staff accountant now working as an analyst, with 3 years in SQL and Power BI on top of 6 years in finance. Built the margin reporting that replaced a 22-tab workbook for a 40-person commercial team, and know close cycles, GL structures and why the numbers have to tie. Looking for a finance-facing analyst role.

3. Experience

This is the section people read. Each job gets a title, employer (or a short description of the employer if the name means nothing outside the region), city and state, and month-and-year dates. Three to five bullets for your current and most recent role, two to four for older ones. Anything more than about ten years back gets one line or nothing.

Bullet formula

Action verb + what you did + the thing it acted on + scope in data-analyst units (rows, tables, dashboards, users, reports, hours, models, tests, cadence) + the decision or outcome where you know it.

Use past tense for finished work and present tense for your current job. Never start a bullet with "Responsible for" — it costs three words and says nothing.

Example bullets you can adapt:

• Built 9 Power BI dashboards for the operations team, replacing a weekly email of 6 Excel attachments.
• Wrote and maintain roughly 60 SQL models in dbt covering orders, returns and fulfilment, with tests on primary keys and row counts.
• Cut the nightly ETL runtime from 3 hours to 48 minutes by rewriting 4 queries and adding incremental loads.
• Analysed 2 years of claims data (about 8 million rows) to identify the 6 denial reasons driving 70 percent of rework, which became the basis for a process change in the billing team.
• Designed and read out 11 A/B tests on checkout flow, including power calculations and a written recommendation for each.
• Defined 23 metric definitions in a shared data dictionary, ending a recurring dispute between marketing and finance over what counted as an active customer.
• Automated a monthly board pack that previously took 2 days of manual Excel work, now generated in under 30 minutes.
• Trained 18 non-technical staff across 3 sessions on self-service reporting, cutting ad hoc data requests to the team by about half.
• Forecast weekly demand for 140 SKUs in Python, with the output feeding the purchasing team's reorder sheet.
• Partnered with 4 engineers to add event tracking to the mobile app, defining 19 events and their properties before launch.

If you do not have a clean outcome number, scope is still a number. "Built a dashboard" is weak; "Built a dashboard used by 40 store managers weekly" is strong even without a dollar result.

4. Skills

One block, grouped, no rating bars and no star systems. A hiring manager wants to know which dialect of SQL you have written and which BI tool you have actually shipped in. Name them:

• Languages and querying: SQL — and say the flavour (Snowflake, BigQuery, Redshift, PostgreSQL, SQL Server, MySQL). Python with pandas and NumPy, or R with tidyverse.
• BI and visualisation: Tableau, Power BI, Looker, Qlik, Google Data Studio / Looker Studio.
• Spreadsheets: Excel, naming the real capabilities — Power Query, pivot tables, XLOOKUP, Power Pivot.
• Pipeline and warehouse: dbt, Airflow, Snowflake, Databricks, Fivetran.
• Methods: A/B testing, cohort analysis, regression, forecasting, data modelling.
• Version control: Git, GitHub or GitLab.

Do not list a tool you could not open and use in an interview. Screen-shares happen, and SQL take-homes are common.

5. Education

Degree, major, institution, graduation year. Put it below experience once you have a year or two of work, above experience while you are a student or new graduate. Include relevant coursework only in the entry-level version — econometrics, database systems, statistics, linear algebra — and drop it after your first analyst job.

GPA is optional. Include it if it is 3.5 or higher and you graduated within about three years; otherwise leave it off. A master's in statistics, economics, analytics or a quantitative field is worth listing; an unfinished degree can be listed as "coursework completed toward" with the years, without implying it was awarded.

6. Licences and certifications

Data analysis is not a licensed occupation in the US, so there is no state board or mandatory credential. What you can list are vendor and platform certifications, which carry real weight when the employer runs that stack:

• Tableau Desktop Specialist, Certified Data Analyst or Certified Consultant
• Microsoft Certified: Power BI Data Analyst Associate, or Azure Data Fundamentals
• Google Data Analytics Professional Certificate, or Google Cloud Professional Data Engineer
• SnowPro Core Certification
• Databricks Data Analyst Associate
• AWS Certified Data Engineer – Associate
• SAS certifications, if you work in pharma, insurance or government where SAS is still standard

List the credential name and the year you earned it. If it has expired or is in progress, say which — "in progress, exam scheduled March 2025" is honest and still useful. Some roles in regulated settings (clinical research, financial services, government contracting) ask about background checks, clearances or HIPAA training; mention those only if you actually hold them, and only in a single line.

If you have no certifications, do not create a near-empty section. Fold a single credential into education instead.

The difference is almost always a missing number and a missing consequence. Four pairs:

Pair 1 — reporting

Weak

Responsible for creating dashboards and reports for the business.

Strong

Built and maintain 14 Tableau dashboards used weekly by around 200 people across merchandising, finance and marketing, replacing a standing email of 6 Excel attachments.

The weak version could describe a two-week intern or a ten-year veteran. The strong one tells you the tool, the count, the audience size and what it replaced.

Pair 2 — pipelines and automation

Weak

Improved the efficiency of data processes and reduced manual work.

Strong

Rebuilt the daily sales pipeline in dbt and Snowflake, cutting the morning refresh from 95 minutes to 22 and removing 3 manual steps that required an analyst online at 6 a.m.

Before-and-after times are the clearest units an analyst has. If you do not have exact minutes, a defensible estimate in a round range is better than "improved efficiency".

Pair 3 — analysis that led to a decision

Weak

Analysed customer data to provide insights to stakeholders.

Strong

Analysed 2 years of subscription data (about 380,000 members) to isolate the 3 onboarding steps with the highest drop-off; the growth team reordered the flow the following sprint.

"Insights" is the single emptiest word on analyst resumes. Name the dataset, name the finding, name who acted on it.

Pair 4 — stakeholder work

Weak

Worked closely with cross-functional teams and communicated findings.

Strong

Present a monthly trading readout to 12 directors and VPs, condensing a 40-page data pack into 5 slides and one recommendation.

Communication claims are worth nothing as adjectives and a lot as a cadence plus an audience. Meeting frequency, seniority of the room, and what you produced are all countable.

Pick the 10 to 14 that are true for you and that match the posting. Listing everything dilutes the ones that matter.

Data analyst pay in the US is usually a base salary, often with an annual bonus tied to company or individual performance. In tech and some finance roles, equity — restricted stock units or options — can form part of the package, and that varies far more between employers than base salary does. Contract and staffing-agency analyst roles are typically hourly, sometimes without benefits, so compare total value rather than the headline rate.

Four things move the number most:

• Location. Metro area matters more than almost anything else, and the gap between major coastal tech hubs and smaller inland markets is substantial. Remote roles often pay by a location band tied to where you live, so ask how the band is set before you assume a hub rate.
• Industry. Technology, finance and consulting generally sit above healthcare delivery, education, nonprofit and government for comparable work. Government and higher education often compensate with pension access, defined pay grades and predictable hours instead.
• Technical depth. Analysts who own pipelines, write production SQL in dbt, or build models tend to be paid closer to analytics engineering than to reporting. Analysts who work mainly in Excel and a BI tool sit lower in most bands.
• Level and scope. Titles run roughly from analyst to senior analyst to lead or staff analyst, and from there into analytics engineering, data science or analytics management. Each step usually requires owning something — a domain, a data product, or other people's work.

For current figures, use the US Bureau of Labor Statistics. The Occupational Outlook Handbook and the Occupational Employment and Wage Statistics (OEWS) program publish wage data by occupation, state and metropolitan area; data analysts are generally covered under data scientists and related occupations, and sometimes under management analysts or operations research analysts, depending on the work. For state-level detail, your state's labor market information office publishes its own projections and wage tables. Company-reported ranges on job postings are also a useful check — many states now require a pay range in the posting itself, so searching postings in the metro you are targeting gives you a live read on the market.

On outlook: demand for analysts tends to follow the broader hiring cycle in whichever industry you target, so a weak market in tech can coexist with steady hiring in healthcare, insurance, logistics or the public sector. Check the BLS projections for the occupation and your state's own forecast rather than relying on general commentary.

Next steps once the resume is done:

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