Chargeback Data Analyst; MX

Puesto: Chargeback Data Analyst (MX)
Location: Santiago Mexquititlán Barrio Primero

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy.

Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here!

Position Overview

You’ll join our Chargeback Investigations team as a Data Analyst, serving as a front-line financial driver and data integrity gatekeeper for Signifyd. In this role, you will leverage data to uncover insights, optimize dispute strategies, and minimize financial losses while mitigating fraud and chargeback risks for our customers.

Using your skills in data analysis, trend identification, and chargeback research, you will help track recovery performance, ensure claims align with card network policies, and deliver actionable data to help merchants and internal teams optimize their workflows for success. This is a high-impact role offering tremendous opportunities for growth, learning, and cross-functional leadership.

What You’ll Do

Work independently and with your team to help achieve the overall goals of the department.

Gather feedback and complete research for new products, features and developments

Enhance our operational workflows via process improvements and identification of automation opportunities.

Oversee quality assurance checks on new features

Support Product teams in different Product and feature launch stages, ensuring alignment on timelines, requirements and success criteria.

Develop metrics and projections on large datasets to extract trends, measure results and outcomes, identify areas of opportunity, and predict future performance.

Turn insights into compelling business cases and recommendations that drive action and support business decisions  based on insights gained from your data analysis.

Build analytical tools and dashboards to quantify the impact of CBI initiatives and identify opportunities for improvement

Develop a deep understanding of how existing metrics are calculated, and ensure metrics source data is reliable and measures our effectiveness at combating integrity issues

Collaborate with data science, BI, and product teams, prioritize issue resolution, and establish and maintain an operating rhythm and accountability to identify and resolve.

Partnering with engineering, product, and merchant teams to fix root-cause vulnerabilities that generate avoidable chargebacks.

Requirements

Fluent in English and Portuguese (written and verbal).

Minimum of 2 years of experience in chargeback analysis, fraud prevention, or payment operations within Lat Am, EMEA, or North American markets.

Proven data analysis capabilities, with experience using quantitative data to identify chargeback trends, optimize dispute win rates, and drive operational decision-making.

Knowledge and experience using SQL, Python, Databricks and Looker

Hands-on experience with payment processors across Lat Am, EMEA, or North America.

In-depth knowledge of card network regulations (Visa, Mastercard, American Express, Discover) specifically surrounding chargeback cycles, dispute rights, and merchant/consumer guidelines.

Strong analytical and problem-solving skills, with the ability to translate complex transaction datasets and dispute metrics into clear actionable insights.

Highly professional and adaptable, capable of keeping pace with changing environments while maintaining strict adherence to Signifyd’s Security and Privacy policies.

Strict discretion and confidentiality when handling sensitive consumer data, financial metrics, and payment details.

Excellent time management and organizational skills, with a proven ability to prioritize heavy data workloads,…