Andrii

Andrii Mamchych | [open contact info](look above in the "contact info" section) | [open contact info](look above in the "contact info" section)

SUMMARY
Data Analyst with 2+ years of hands-on experience in product and marketing
analytics, specializing in subscription-based products and unit economics. Expert in
SQL, Python, and Power BI, with a strong track record of building analytics from
scratch and conducting A/B tests. I turn raw data into product impact - guiding
Product and Marketing teams to optimize acquisition, improve retention, and drive
revenue growth.

SKILLS
Technical: Python (pandas, numpy, matplotlib, seaborn, scipy), SQL,
PostgreSQL, MySQL, Power BI, Tableau, MS Excel, Mixpanel, RevenueCat,
SuperWall, Google Analytics (GA4), GTM, Git/GitHub.
Analytics & Metrics: Product Analytics, A/B Testing, Cohort & Funnel Analysis,
Unit Economics (LTV, CAC, Retention, Churn, ARPU, ROAS, MRR), Marketing
Attribution.
Languages: English (B2 - Upper-Intermediate), Ukrainian (Native).

EXPERIENCE
Data Analyst Mobile App Startup (Remote) | Sep 2024 - Sep 2026
Architected and rolled out a scalable product analytics taxonomy (Mixpanel,
RevenueCat) for behavioral and subscription tracking - establishing a 100%
accurate data flow for core unit metrics (MRR, ARPU, Churn).
Designed, executed, and analyzed 20+ end-to-end A/B experiments on
monetization paywalls (SuperWall, Python) - providing actionable insights that
directly increased free-to-paid conversion rate by 14%.
Designed and maintained data extraction pipelines (SQL, Python) to clean and
aggregate raw product data - completely eliminating data silos between
Product and Marketing teams.
Built numerous (>15) extensive self-serve dashboards using Power BI, Tableau,
and MS Excel to automate daily reporting for Unit Economics (LTV, CAC, ROAS)
and cut weekly reporting time by >10 hours.
Optimized ad spend and identified the most profitable acquisition channels by
implementing reliable Web2App tracking and marketing attribution models
(GA4, GTM).
Increased key metrics significantly (Day-7 Retention by 9%) by conducting
deep-dive exploratory data, cohort, and funnel analysis (Python) to identify and
fix critical user drop-off points during onboarding.

EDUCATION
Bachelor in Software Engineering (Focus: Data Engineering & Analytics) | Lutsk
National Technical University