Data Analyst Resume Example

Sam Chen

[email protected] • (555) 278-0000 • Seattle, WA • linkedin.com/in/samchen

Professional Summary

Data analyst with 6 years turning complex datasets into decisions that move the business forward. Specializes in Python-based ETL pipelines, SQL optimization, and interactive Tableau dashboards used by executive and operational teams. Consistently recognized for translating data noise into clear, actionable narratives without losing statistical rigor.

Experience

Senior Data Analyst Cascade Retail Group — Seattle, WA | 2022–Present

Built an inventory demand-forecasting model achieving 89% accuracy across 12,000 SKUs, reducing overstock costs by USD 4.1M in the first year of deployment.
Designed and maintain a real-time operations dashboard used by 45+ store managers, replacing 6 disconnected spreadsheets and cutting weekly reporting prep from 8 hours to 20 minutes.
Ran an A/B testing program for email promotions across a 200,000-subscriber list; incrementally optimized revenue per email from USD 0.22 to USD 0.41 over three cohorts.
Automated the monthly sales reconciliation pipeline (Python + Airflow), eliminating 12 hours of manual data entry per month and reducing reconciliation errors from 4.2% to 0.1%.

Data Analyst Vertex Insurance — Portland, OR | 2020–2022

Developed a churn prediction model with 83% precision, enabling the retention team to prioritize outreach for the top 15% highest-risk policyholders and reduce annual churn by 11%.
Refactored 40+ legacy SQL queries, reducing average executive dashboard load time from 12 seconds to under 2 seconds across all reporting surfaces.
Created mobile app onboarding analytics that identified a friction point in step 3 of 5; product team's fix improved Day-7 retention from 22% to 38%.

Data Analyst Lumina Media — Seattle, WA | 2018–2020

Built Python scripts to automate weekly audience reporting, saving 5 hours of manual work per week across a 3-person editorial team.
Identified USD 180,000 in wasted ad spend through placement-level attribution analysis; redirected budget to high-performing placements with 2.8x better CPM efficiency.

Education

B.S. Statistics University of Washington — 2018

Technical Skills

Languages: Python (pandas, NumPy, scikit-learn), SQL, R • Databases: Snowflake, PostgreSQL, BigQuery • Visualization: Tableau, Looker, Excel (advanced, pivot tables) • Pipelines: dbt, Airflow, Google Analytics 4 • Methods: A/B testing, regression, forecasting, cohort analysis

Why this resume works

Dollar-value outcomes appear in three different bullets (USD 4.1M cost reduction, USD 0.41 revenue per email, USD 180K recovered ad spend), making the scale of business impact unmistakable.

The forecasting model bullet specifies both the accuracy metric (89%) and the business result (USD 4.1M savings), which are the two questions every hiring manager asks: "How good is your model?" and "What did it actually do?"

Dashboard and reporting bullets include the audience size (45+ managers, executive team), establishing that the analyst's work influenced real decision-makers at organizational scale.

Technical skills are organized by category (Languages, Databases, Visualization, Pipelines) rather than a flat keyword dump, making the candidate's toolchain legible at a glance.

Tool names match ATS keywords exactly: Snowflake, dbt, Airflow, Google Analytics 4, scikit-learn appear as they do in data job descriptions.

3 tips for data analysts in 2026

Show that you drive decisions, not just deliver reports The most valued analysts in 2026 are those who influence what the business does next. Frame every project around the decision it enabled: "analysis led to retention team prioritizing X" beats "conducted churn analysis." The former shows judgment; the latter shows task completion.

Add at least one ML or predictive modeling bullet The line between data analyst and data scientist is blurring. Even basic regression or classification models on job-relevant data signals you can move up the value chain. If you have built one in Python or R — even a simple one — put it front and center.

Name the data warehouse, not just "SQL" Snowflake, BigQuery, Redshift, and Databricks each have distinct architectures that companies care about. Listing "SQL" alone leaves hiring managers guessing. Specify the platforms you have worked with; they are often the exact keywords recruiters filter on before a human reviews the resume.