About the position
Riot engineers bring deep knowledge of specific technical areas but also value the opportunity to work across a wide range of domains. As a Senior Software Engineer, you'll dive into cross-functional projects that contribute to team cohesion and advance broader product goals. You'll lead by example, providing other engineers with a strong model of technical excellence, thoughtful collaboration, and continuous improvement. Revenue Protection ingests payment data from multiple global partners, reconciles every transaction end-to-end, and emits real-time fraud signals that safeguard player trust and Riot's bottom line. We lean on CloverDX for ETL and Java/Kotlin microservices for business logic, and we're expanding into Kafka streaming and machine-learning-driven trust-list and velocity-check models. Your architecture and coding skills will be central to making those capabilities real and multiplying our fraud-prevention impact. You will report to the team Software Engineering Manager.
Responsibilities
- Design & evolve CloverDX pipelines that ingest, transform and validate high-volume payment data.
- Build Java/Kotlin microservices (Spring Boot, REST/async messaging) that power real-time reconciliation and fraud decisions.
- Help bring ML-driven fraud-detection models to production.
- Automate deployment & observability—CI/CD for Clover graphs, Docker/K8s releases, Datadog alerts, runbooks and SLOs.
- Champion operational excellence: on-call rotation, incident triage, cert rotation, IAM/ACL hygiene for PCI-scope systems.
- Mentor teammates & unblock analysts in SQL, Python and CloverDX best practices.
Requirements
- 5+ years of professional software-engineering experience.
- Proficiency in Java or Kotlin and designing distributed, fault-tolerant services.
- Hands-on expertise with ETL/ELT platforms—CloverDX strongly preferred.
- Strong SQL and data-modelling skills for financial reporting.
- Demonstrated ownership of live services: SLOs, on-call, RCAs.
Nice-to-haves
- Experience working on data- or finance-critical systems.
- Experience building or integrating machine-learning models for classification, risk scoring or anomaly detection.
- Bachelor's degree in Computer Science or related field.
- Working knowledge of payment-processing flows (auth, capture, chargeback) and fraud-risk concepts (velocity checks, trusted-buyer lists).
- Expertise with Kafka or other streaming platforms.
- Familiarity with observability stacks (Datadog, Prometheus).
- Python scripting for data-quality checks, CI test harnesses and ops automation (e.g., self-healing ETL).
Benefits
- Open paid time off policy.
- Flexible work schedules.
- Medical, dental, and life insurance.
- Parental leave for you, your spouse/domestic partner, and children.
- 401k with company match.
