Data Analyst - Payment Optimization

dlocal; Buenos Aires; Hybrid; ARS 1.200.000 - 2.100.000; Vollzeit

Zusammenfassung

dLocal, a leading payments fintech, seeks a Data Analyst in Argentina. You will learn daily, analyze transaction data, and become a key source of insights to drive decisions and optimize payment flows for customers such as Amazon and Spotify.

You will collaborate with Product, ML, Operations and Customer Success to deliver data-driven improvements, build dashboards, and communicate results in English.

Qualifikationen

• Analytically driven with large data sets in SQL and deriving actionable insights.
• 1+ years of payments analytics experience.
• Strong SQL skills and ability to query relational databases; Python/R a plus.
• Excellent communication and presentation in English.

Aufgaben

• Analyze transaction data for customers and identify improvement opportunities.
• Coordinate AB tests and monitor KPIs to optimize payment flows.
• Partner with Product, ML, Operations and Customer Success to implement solutions.

Kenntnisse

SQL

Data analysis

A/B testing

Dashboards

English

Ausbildung

Quantitative degree

Tools

Python

R

Jobbeschreibung

Why Join dLocal?

dLocal is the financial infrastructure powering global commerce in the world's fastest-growing markets. The biggest companies in the world trust us to unlock growth in 60+ countries across emerging markets—moving money where others see complexity. We don't just process payments; we are architects of payment ecosystems and partners in our customers' expansion. You'll work alongside 1,300+ teammates from 40+ nationalities and tackle global challenges from day one.

What's the opportunity?

We are looking for a great Data Analyst who is eager to learn new things every day. If you love solving complex problems involving data analysis and being a key source of insights for high-impact decisions, In this role, you'll be responsible for driving data analytics across different payment flows. You’ll be monitoring, understanding, and building a scalable continuous optimization operation to maximize payment conversion rates.

What will I be doing?

Deeply understand how our customers such as Amazon, Spotify, Booking, or Uber are using dLocal to process payments. Become a key player during the ramp-up of new customers, by closely analyzing transaction information and identifying areas of improvement. This involves examining millions of data points and recognizing patterns of why some transactions fail. Coordinate and run A/B tests to continuously improve key performance indicators. Work closely and be the source of insights for our Product, Machine Learning, Operations and Customer Success teams in order to develop new solutions that will increase the success of our customers.

What skills do I need?

Analytically driven with experience manipulating large data sets in SQL or equivalent, and drawing actionable insights from data. 1+ years of experience in payments/product analytics. Extensive experience and passion for metrics and dashboards visualizations Highly skilled at writing SQL to query relational databases; knowledge of R or Python is a plus Strong communication and presentation skills (English is a must). Degree in a quantitative field (e.g., Statistics, Economics, Math, Sciences, Engineering, Data Science)

What do we offer?

Besides the tailored benefits we have for each country, dLocal will help you thrive and go that extra mile by offering you:

• Flexibility in how you work: We focus on impact and productivity over fixed hours. This means our teams have flexible schedules and, depending on your role and location, you will combine self‑managed focus time with moments of in‑person connection in our collaboration hubs.
• Fintech industry: work in a dynamic and ever-evolving environment, with plenty to build and boost your creativity.
• Referral bonus program: our internal talents are the best recruiters - refer someone ideal for a role and get rewarded.
• Work From Anywhere: Team members can work while traveling for up to 3 months every year.