Written by Argishti Yeghiazaryan

If your resume gets a 90-second scan, can someone spot a North Star metric you moved and 2-3 experiment wins with clear deltas? This guide shows exactly how to surface those signals, map them to AARRR, and pass ATS while staying honest about data and scope.

Your 90-second scan and the Growth Evidence Stack

What hiring managers skim first:
- Title and scope match: Growth Marketing Manager, Senior Growth, Head of Growth
- North Star moved: activation rate, retained M0-M3, LTV:CAC, revenue growth
- 2-3 experiments with deltas: +X% at Y significance, cohort lift, payback shortened
- Tools you can actually run: GA4, Amplitude, Mixpanel, Braze, HubSpot, Marketo, Segment, Optimizely, SQL
- Company stage and motion: PLG SaaS, paid-led e-commerce, marketplace, fintech

Build your Growth Evidence Stack on page 1:
- North Star: 1-2 metrics tied to business model, example LTV:CAC 3.1x to 3.8x in 2 quarters
- AARRR map: 1-2 bullets per stage you influenced
- Top experiments: hypothesis, sample size, result, learning
- Systems built: lifecycle cadences, attribution model, pricing test harness, referral engine
- Collaboration: product, data, eng, sales, finance touchpoints
- Tool depth: verbs that prove use, not a logo list
- Scale: budgets, volumes, regions, segments, SKUs or plans
- Guardrails: compliance, experiment quality, shipped decisions

Map achievements to AARRR without fluff

Tie each bullet to a stage so your impact is easy to parse.

Acquisition
- Metrics: CAC, CTR, CVR, ROAS, CAC payback, blended vs paid CAC
- Example: Scaled Meta and Google to $800k monthly spend while holding blended CAC flat and improving payback from 7.5 to 5.9 months

Activation
- Metrics: signup-to-setup, PQA or PQL rate, activation within 7 days, time-to-value
- Example: Redesigned onboarding checklist to match aha moment actions, raising 7-day activation from 22% to 31%

Retention
- Metrics: D30-D90 retention, churn rate, expansion rate, NPS, feature adoption
- Example: Launched proactive lifecycle nudges tied to feature gaps, cutting 90-day churn from 14% to 9%

Revenue
- Metrics: ARPU, MRR, ARR, expansion revenue, trial-to-paid, payback, LTV:CAC
- Example: Introduced 2-tier pricing test that lifted ARPU by 11% without impacting trial-to-paid

Referral
- Metrics: K-factor, referral share of signups, CPA via referral, time-to-2nd referral
- Example: Built milestone-based referral program that drove 18% of new signups at one-third the paid CAC

Formatting tip
- Start with the outcome, add scope, then insight. Include baseline and period. Keep to one line where possible.

Show experiments concisely on a resume

Use a compact structure that proves rigor.

Pattern
- Hypothesis: If we add usage-based prompts before paywall, activation will rise
- Design: 50-50 split, pre-qualifying new signups, tracked in Amplitude with event schema v2
- Sample size and power: n=18k, 95% power calculated via historical variance
- Result: +13% activation, +6% trial-to-paid, no lift in support tickets, 93% stat significance
- Decision: Shipped to 100% and moved trigger to day 1

Resume line example
- Ran onboarding paywall experiment at n=18k using Amplitude events v2 and Optimizely, lifting 7-day activation by 13% at 93% significance and trial-to-paid by 6%, shipped globally

If space is tight, collapse design details and keep hypothesis, result, and tool stack verbs.

Quantify LTV, CAC, ROAS, and retention without disclosing secrets

You can be precise without sharing confidential numbers.

Use indexed baselines
- Indexed LTV moved from 1.00 to 1.22 in two quarters

Use ratios and ranges
- Improved LTV:CAC from 2.6x to 3.3x
- Shortened CAC payback from 8-9 months to 5-6 months

Use deltas with timeframes
- Lifted 90-day retention by +4.2 points quarter over quarter

Use directional revenue without absolute numbers
- Expanded ARR by mid single digits via pricing test

If numbers are highly sensitive
- Present per-cohort percentage change and confidence interval
- State anonymized segment labels, example Segment A high-intent, Segment B low-intent
- Note audit or review, example results reviewed with finance weekly

Specialization tracks and ATS keywords that signal fit

Pick one or two tracks as your spine, add supporting skills.

PLG and product-led growth
- Keywords: PQL, PQA, activation, onboarding, aha moment, usage-based pricing, freemium, paywall testing, in-product messaging, event schema, cohort analysis

Paid acquisition and performance
- Keywords: ROAS, CAC, MER, LTV modeling, creative testing, incrementality, MMM, UGC, Appsflyer, Branch, SKAN, audience segmentation

Lifecycle and CRM
- Keywords: Braze, Iterable, HubSpot, Marketo, customer journeys, triggered messaging, deliverability, segmentation, winback, churn prediction, RFM, frequency capping

SEO and content-led growth
- Keywords: topical authority, content briefs, technical SEO, log file analysis, schema markup, programmatic SEO, internal linking, conversion copy, content velocity

Referral and loyalty
- Keywords: K-factor, referral loops, milestone rewards, fraud prevention, invite funnels, loyalty tiers, cashback, advocacy

Growth analytics
- Keywords: GA4, Amplitude, Mixpanel, BigQuery, Snowflake, dbt, Looker, SQL, attribution, event taxonomy, experimentation, power analysis, Bayesian A/B

Use these keywords in context with actions: built, automated, modeled, shipped, scaled, audited.

Tools you list must show depth, not brand drops

Tie tools to outcomes and ownership.

Analytics and attribution
- GA4, Amplitude, Mixpanel, Heap, Appsflyer, Branch, Singular, Segment, mParticle
- Prove depth: defined event taxonomy, reconciled channel vs product events, built cohorts for lifecycle triggers

Experimentation
- Optimizely, VWO, LaunchDarkly, Eppo
- Prove depth: owned guardrails, shipped holdouts, ran CUPED or sequential tests when needed

Lifecycle
- Braze, Iterable, HubSpot, Marketo, Customer.io
- Prove depth: built multi-channel journeys, improved deliverability, set frequency caps, templated localized content

Data and BI
- BigQuery, Snowflake, Redshift, dbt, Looker, Tableau
- Prove depth: modeled activation funnel, built LTV cohorts, scheduled refreshes, versioned metrics

Paid media
- Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads, Apple Search Ads, DV360
- Prove depth: creative testing framework, structured naming, brand safety lists, automated budget pacing

Engineering collaboration
- Jira, Git with basic PRs, Postman for API checks
- Prove depth: wrote tickets with acceptance criteria, validated events against schema, QAed experiments

SQL and light Python for growth marketers

Including SQL is useful if you use it to ship decisions.

What to claim credibly
- SQL: CTEs, window functions, cohort tables, funnel steps, deduplication, joins across events and users
- Python: notebooks for EDA, cohort pulls, simple propensity models, lightweight scripts for report automation

Resume lines that prove it
- Wrote SQL in BigQuery to build weekly retention cohorts and reconcile GA4 sessions with backend events, driving lifecycle triggers that lifted D30 retention by 3 points
- Prototyped churn risk model in Python and handed to data science for productionization, improving winback CTR by 19%

Keep it short, connect to a shipped change, and avoid listing libraries you did not use.

Industry module: SaaS and PLG signals to highlight

What matters
- Activation to PQL rate, trial-to-paid, seat expansion, product-qualified accounts for sales assist motions
- Pricing tests that shift ARPU without hurting activation
- In-product prompts, checklists, and aha moment discovery

Bullet bank
- Increased PQL conversion by 24% by gating advanced exports post aha moment and personalizing setup tips
- Reduced time-to-value from 2.8 days to 1.6 days by surfacing sample data and guided tours
- Drove 9% ARR uplift via seat bundle experiment with guardrails to avoid logo churn

Quality signals
- Event schema versioning, journey-led trigger design, downstream impact monitored in finance-reconciled ARR

Industry module: Marketplace growth with balanced supply and demand

What matters
- Solving the chicken-and-egg per geo or category
- Liquidity metrics: fill rate, time-to-first-transaction, repeat rate by side
- Trust and safety programs impacting growth

Bullet bank
- Launched city playbooks that lifted supply activation by 17% and reduced time-to-first-job to 3.1 days
- Implemented two-sided referral tiers, raising K-factor to 0.31 while limiting fraud to under 1.5% via velocity checks
- Introduced badging for top providers, increasing request-to-accept by 22%

Quality signals
- Geo-level cohorting, seasonality controls, fraud and compliance gates documented

Industry module: E-commerce performance and lifecycle

What matters
- MER and ROAS discipline, list growth and deliverability, repeat purchase cadence, AOV levers
- Attribution realism across paid, email, SMS, and organic

Bullet bank
- Scaled TikTok and Meta creatives via UGC pipeline, improving blended MER from 2.1 to 2.6 at $1.2M monthly spend
- Built post-purchase flows that lifted 60-day repeat purchase rate from 19% to 26%
- Shipped SKU-specific pricing tests and bundles, raising AOV by 12% without volume loss

Quality signals
- Holdouts for email and SMS, SKU and category level ROAS reporting, returns impact tracked

Industry module: Fintech growth with compliance-aware rigor

What matters
- Compliance and risk checks, KYC completion, funded account activation, CAC payback under strict limits
- Mobile attribution complexity and SKAN constraints

Bullet bank
- Increased KYC pass-through by 9 points by simplifying document upload and adding educational nudges
- Shortened funded activation window from 7.2 to 4.5 days via triggered in-app tasks
- Built fraud-aware referral with velocity and device fingerprint checks, growing referrals to 14% of signups at one-fourth paid CAC

Quality signals
- Risk thresholds agreed with compliance, SKAN model validation, legal-reviewed copy and claims archived

Career-level scorecards and summary examples

What hiring teams expect by level

Associate or Junior
- Can run channel ops, basic analysis, QA events, write clean briefs, document learnings
- Summary example: Associate growth marketer with 1 year in e-commerce, executed paid and email tests across 3 brands, improved CTR by 18% via creative refresh and fixed GA4 event gaps to enable funnel reporting

Growth Marketing Manager
- Owns a funnel segment, runs experiments, coordinates with product and lifecycle, manages budgets
- Summary example: Growth Marketing Manager focused on activation and paid media, scaled spend to $600k per month at stable CAC and lifted trial-to-paid by 5 points via onboarding optimizations

Senior or Lead
- Defines strategy for stage, builds systems, coaches others, partners with product and data
- Summary example: Senior growth marketer specializing in PLG SaaS, built PQL framework, shipped pricing tests, improved LTV:CAC from 2.7x to 3.4x across two quarters

Head or Director
- Sets roadmap, hires, builds operating cadence, allocates capital, aligns exec stakeholders
- Summary example: Head of Growth with marketplace experience, drove liquidity improvements across 12 cities, instituted experimentation guardrails, and raised GMV by low double digits with stable take rate

VP Growth
- Owns multi-quarter growth strategy, portfolio of bets, org design, finance alignment
- Summary example: VP Growth for B2B SaaS at Series C, integrated lifecycle and product growth motions, advanced attribution and ARR forecasting, led team of 18 across paid, PLG, and analytics

Weak-to-strong bullet makeovers

Example 1:
Weak:
- Managed email campaigns to improve retention
Strong:
- Built Braze journeys with behavior triggers, lifting 60-day repeat purchase rate from 21% to 27% and cutting unsubscribe rate by 18%
Why it works:
- Names tool, trigger logic, baseline and delta, and the retention window

Example 2:
Weak:
- Optimized onboarding for better activation
Strong:
- Ran setup checklist experiment at n=22k in Amplitude, increasing 7-day activation by 10% and trial-to-paid by 4% without adding support volume
Why it works:
- Shows sample size, platform, two outcomes, and a guardrail

Example 3:
Weak:
- Managed a $500k ads budget
Strong:
- Scaled Meta and Google to $500k monthly with creative testing and audience exclusions, improving blended MER from 2.0 to 2.5 and cutting CAC payback from 7 to 5 months
Why it works:
- Adds tactics, blended metric, and a business-relevant payback improvement

Complete resume example

The candidate, companies, and career history shown are fictional examples created for illustration and any resemblance to a real person or organization is coincidental.

Talia Ng
Target Position: Senior Growth Marketing Manager, PLG SaaS
Location: Singapore, open to remote APAC and EMEA

Professional Summary
Senior growth marketer with 7 years across PLG SaaS, e-commerce, and fintech. Built activation and lifecycle systems that lifted 90-day retention by 4-6 points and improved LTV:CAC from 2.6x to 3.3x. Strong in Amplitude, Braze, GA4, and SQL. Partnered with product and data to ship experiments at scale.

Grouped Skills
- Growth strategy: activation, onboarding, pricing tests, referral loops
- Experiments: hypothesis design, power analysis, CUPED, sequential testing
- Channels: paid search and social, SEO, lifecycle email and in-app
- Analytics: GA4, Amplitude, Mixpanel, Looker, BigQuery, SQL
- Lifecycle: Braze, HubSpot, deliverability, segmentation, frequency caps
- Tooling: Segment, Optimizely, LaunchDarkly, Appsflyer
- Collaboration: product, engineering, data science, finance, legal

Professional Experience

Lumenly, Senior Growth Marketing Manager, SaaS PLG, Singapore, 2023-2026
- North Star: improved PQL-to-paid from 23% to 30% and LTV:CAC from 2.7x to 3.5x in 3 quarters
- Ran onboarding paywall and checklist experiments at n=18k-25k using Optimizely and Amplitude, raising 7-day activation by 13% and trial-to-paid by 6%
- Built lifecycle in Braze with event-based triggers and frequency caps, cutting D90 churn from 13% to 9% and increasing expansion revenue by 8%
- Partnered with product to test usage-based add-ons, lifting ARPU by 9% with no activation loss
- Modeled cohort LTV in BigQuery and reconciled with finance to guide budget allocation

HorizonPay, Growth Analyst, Fintech, Remote, 2021-2023
- Shortened funded account activation from 7.1 to 4.6 days via in-app tasks and KYC nudges
- Increased KYC pass-through by 8 points by simplifying document upload steps after event audit
- Built fraud-aware referral with device velocity checks that drove 12% of signups at one-fourth paid CAC
- Implemented SKAN-informed iOS bidding strategy and held blended CAC flat while scaling 40%

Shopbird, Performance Marketing Associate, E-commerce, Kuala Lumpur, 2019-2021
- Managed $350k monthly across Google, Meta, and TikTok, improving MER from 1.9 to 2.4
- Shipped creative testing framework that lifted CTR by 22% and lowered CPC by 14%
- Partnered with CRM team to align promo calendar and post-purchase journeys, boosting 60-day repeat by 5 points

Education and Training
- B.S. Economics, University of Malaya
- SQL for Data Analytics, completed 2021

Certifications
- Google Analytics Individual Qualification
- Braze Certified Marketer
- Meta Blueprint Media Buying Professional

FAQ: specific questions growth marketers search for

How do I show experiments without writing long case studies on the resume?
- Use one line per experiment: outcome first, sample size, tool, significance, and decision shipped

What metrics matter most and how do I avoid vanity metrics?
- Tie to AARRR and bottom line: activation, retention, LTV:CAC, payback, expansion. Avoid only clicks and impressions unless linked to CAC or revenue

Which ATS keywords differ for PLG vs paid vs lifecycle vs SEO?
- PLG: PQL, activation, in-product messaging. Paid: ROAS, incrementality, SKAN. Lifecycle: Braze, segmentation, deliverability. SEO: technical SEO, internal linking, programmatic content

How do I quantify LTV or retention if I cannot share numbers?
- Use indexed baselines, ratios, or point changes over a timeframe, example LTV index +18% in 2 quarters, D90 retention +3.8 points

Is SQL worth listing and how do I prove it?
- Yes if you use it to ship decisions. Add a line that links a SQL-built cohort or funnel to a shipped lifecycle or pricing change

Final checklist, compact ATS bank, and submission tips

One-page 2026 checklist
- Contact block: name, city and remote openness, email, phone, portfolio link
- Title: target role, example Senior Growth Marketing Manager
- North Star: 1-2 lines with deltas and timeframes
- AARRR map: 3-6 bullets with metrics
- Experiments: 2-3 bullets with n, tool, result, decision
- Tools: 6-10 with proof verbs
- Scale: budgets, volumes, geos
- Quality: significance, guardrails, compliance
- Education and relevant certs

Compact ATS keyword bank
- PLG: PQL, activation, onboarding, in-product messaging, paywall testing, usage-based pricing
- Paid: ROAS, CAC, MER, incrementality, SKAN, MMM, audience exclusions, UGC
- Lifecycle: Braze, Iterable, segmentation, deliverability, winback, frequency caps
- Analytics: GA4, Amplitude, Mixpanel, BigQuery, Snowflake, dbt, Looker, SQL
- SEO: technical SEO, schema, programmatic content, internal linking
- Referral: K-factor, fraud checks, milestone rewards, invite funnel
- Experimentation: Optimizely, power analysis, CUPED, Bayesian

Portfolio and case study outline
- Problem and metric: what you aimed to move and why it mattered
- Hypothesis and design: audience, sample, guardrails
- Analysis and result: deltas, confidence, secondary effects
- Decision and impact: shipped change, follow-up measurement
- Your role: ownership, collaborators, tools

Submission tips for global and remote
- Save as PDF. File name: FirstLast_GrowthMarketing_2026.pdf
- Use a simple font and clean layout without graphics that break ATS
- Include working links with UTM-free clean URLs
- Match job language once without keyword stuffing

Next action
- Draft your Growth Evidence Stack on page 1, convert two recent wins into one-line experiment bullets, and ship a PDF version today.