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're looking for a seasoned Data Specialist with at least 5 years of experience to join our Compliance team, focused on Anti-Money Laundering (AML) and Countering the Financing of Terrorism (CFT) transaction monitoring. You'll own the analytical backbone of our transaction monitoring systems, and actively build the broader data strategy work within Compliance — whether through liaising with a team of data scientists or through influencing Product teams working upstream to us. This is a hands-on role with real influence on how we detect financial crime across LATAM, EMEA, and APAC.
What will you be doing?
Own the end-to-end analytical lifecycle of our rule-based transaction monitoring systems, from data pulling and exploration in Databricks to proposing rule and threshold changes in alignment with Compliance Leadership requirements.
Maintain and refine our false negative methodology — how we identify what monitoring is missing, not just what it catches.
Help shape the agentic monitoring function — from orchestration design to implementation — as we expand automation across our AML/CFT monitoring flows.
Act as the technical liaison with data scientists for ML driven detection: gather and document requirements, challenge and validate models delivered, and ensure outputs are usable for compliance decision-making.
Own regulatory reporting for AML/CFT transaction monitoring, ensuring the underlying data is accurate, complete, and traceable — proactively identifying and resolving data quality issues before they become reporting or audit problems.
Build and maintain audit-ready documentation for every methodology change — rationale that can be defended to a regulator or auditor months after the decision was made, by someone who wasn't in the room.
Engage with Compliance Leadership to translate evolving AML/CFT regulatory requirements into concrete data and monitoring needs.
Generate reports and dashboards that communicate overall Framework performance and monitoring effectiveness to both technical and non-technical stakeholders.
What skills do you need?
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or proven experience in a correlated field.
5+ years of experience in data analytics, risk, or fraud/financial crime analytics, ideally in financial services or fintech. Deep AML/CFT transaction monitoring experience is a strong plus but not mandatory.
Strong SQL and Python (Databricks environment preferred); comfort with statistical analysis — this is not exclusively a data scientist role, so ML frameworks are a plus, not a requirement.
Solid understanding of the end-to-end data lifecycle — from raw data and pipelines to modeling and reporting — enough to collaborate effectively with Data Engineers, Data Analysts, and Data Scientists.
Demonstrated experience as a technical liaison or requirement-owner working with additional Data, Product and Engineering teams — someone who can push back on a delivery that doesn't meet the need, not just accept what's handed over.
Experience producing documentation that holds up under audit or regulatory scrutiny is a strong plus.
Proactive, collaborative mindset; comfortable operating with a high degree of autonomy on AML data analytics, as part of a small and growing team.
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.
Skills Required
- Bachelor's or master's degree in Computer Science, Data Science, Statistics, Engineering, or a related field, or equivalent proven experience
- At least 5 years of experience in data analytics, risk, fraud analytics, or financial crime analytics
- Strong SQL and Python skills
- Experience working with Databricks or a similar data analytics environment
- Understanding of the end-to-end data lifecycle, including data pipelines, modeling, and reporting
- Experience collaborating with Data, Product, and Engineering teams as a technical liaison or requirements owner
- Experience with AML/CFT transaction monitoring, preferably in financial services or fintech
- Experience producing documentation suitable for audit or regulatory scrutiny
- Statistical analysis experience
- Experience with machine learning frameworks
What We Do
dLocal started with one goal – to close the payments innovation gap between global enterprise companies, and customers in emerging economies. We have over 900 payment methods, in more than 40 countries. With the ability to accept local payment methods and facilitate cross-border fund settlement worldwide, our merchants reach billions of underserved consumers in the high-growth markets of Africa, Asia, and Latin America. dLocal offers the ideal payment solutions for global commerce: Payins: Accept local payment methods Payouts: Compliantly send funds cross-border Defense Suite: Manage fraud effectively dLocal for Platforms: Unify your platform’s payment solution Local Issuing: Localize payments for your gig-economy workers, suppliers, and partners
