Resumes / Primary Jordan 2026
Primary Jordan 2026
Tailor it to a target role, then let the analysis push you past the recruiter's bar.
Tailoring this resume to
No role selected — pick one to optimise against
+ Add a new job to tailor against
Jordan Riley
Headline
Contact & title
Sr. Data Analyst · Sydney, Australia · jordan.riley@email.com · (415) 555-0192
linkedin.com/in/jordanriley · github.com/jordanriley-data
linkedin.com/in/jordanriley · github.com/jordanriley-data
Summary
Profile summary
?
How a winning summary is built 3-PARAGRAPH RULE
A recruiter scans this in under 10 seconds. Write yours first — these are the rules our AI follows too.
1Who you areTitle + years + industries + core value. Factual, no adjectives.
2Skills & impactMirror the role's tools. Action + Achievement + Metric. ≥2 numbers.
3Fit & motivationTie to the target company. End forward-looking, never salesy.
110–130 words≥2 metricsAustralian Englishno "results-driven"no "proven track record"no "responsible for"
Jordan Riley is a results-driven Senior Data Analyst with over 8 years of experience transforming complex datasets into actionable business insights across fintech, e-commerce, and SaaS industries. Known for bridging the gap between raw data and strategic decision-making, Jordan has a proven track record of designing scalable analytics pipelines, building executive-level dashboards, and leading cross-functional data initiatives that directly influence revenue growth and operational efficiency.
Throughout their career, Jordan has partnered closely with product, engineering, marketing, and finance teams to define KPIs, build self-serve reporting infrastructure, and democratize data access across organizations. He bring deep expertise in SQL, Python, and BI tooling, combined with strong business acumen and communication skills that translate technical findings into clear narratives for both technical and non-technical stakeholders.
Jordan thrives in fast-paced, ambiguous environments and is passionate about building data cultures from the ground up, whether that means establishing a company's first data warehouse or mentoring junior analysts toward data maturity. 182 words · 0 metrics Editing — click away to save
Throughout their career, Jordan has partnered closely with product, engineering, marketing, and finance teams to define KPIs, build self-serve reporting infrastructure, and democratize data access across organizations. He bring deep expertise in SQL, Python, and BI tooling, combined with strong business acumen and communication skills that translate technical findings into clear narratives for both technical and non-technical stakeholders.
Jordan thrives in fast-paced, ambiguous environments and is passionate about building data cultures from the ground up, whether that means establishing a company's first data warehouse or mentoring junior analysts toward data maturity. 182 words · 0 metrics Editing — click away to save
Experience
Details
⋮⋮Led the redesign of the company's core reporting infrastructure, migrating from legacy spreadsheets to a centralized dbt + Snowflake data warehouse, reducing report generation time by 73%✕
⋮⋮Developed and maintained 40+ production dashboards in Looker used daily by C-suite, product, and growth teams to monitor KPIs across acquisition, activation, retention, and revenue✕
⋮⋮Partnered with the product team to define and measure A/B test frameworks, directly contributing to an 18% uplift in user activation rate through data-informed feature prioritization✕
Details
⋮⋮Designed end-to-end analytics tracking for the company's e-commerce platform using Segment + BigQuery, covering the full funnel from awareness through post-purchase✕
⋮⋮Created a dynamic pricing sensitivity model that helped the merchandising team optimize discount strategies, contributing to a 9% increase in gross margin over two quarters✕
⋮⋮Automated weekly executive reporting using Python and Google Sheets API, replacing a manual 6-hour process with a 15-minute scheduled pipeline✕
Skills
Analytics & BI Tools
SQL advanced ✕
Python ✕
pandas ✕
NumPy ✕
scikit-learn ✕
matplotlib ✕
R ✕
Looker ✕
Tableau ✕
Power BI ✕
Google Data Studio ✕
Excel / Google Sheets ✕
+ Add skill
Data Engineering & Infrastructure
dbt ✕
Snowflake ✕
BigQuery ✕
Redshift ✕
Airflow ✕
Segment ✕
Fivetran ✕
Stitch ✕
Google Analytics 4 ✕
Mixpanel ✕
+ Add skill
Education
Certifications
⋮⋮
2022
✕
Google Data Analytics Professional Certificate
Google · Coursera
⋮⋮
2023
✕
dbt Analytics Engineering Certification
dbt Labs
⋮⋮
2023
✕
AWS Certified Cloud Practitioner
Amazon Web Services
AI Resume Analysis
40/100
Needs work
Covers the basics. Clear the fixes below to earn the recruiter call.
✦ One-click optimise
Apply every recommendation against the role and our resume best-practice rulebook, then rescore — instantly.
68
Content impact
Strength & evidence of claims
72
Keyword match
Alignment to the target role
90
Formatting
ATS can parse it cleanly
65
Readability
Clear, skimmable, recruiter-ready
What's holding the score back 3 issues
Buzzwords & filler"results-driven", "proven track record"
Fix →No measurable impactSummary has 0 metrics — needs ≥2
Fix →Missing role keywordsdbt, Airflow, Looker, Snowflake
Fix →