Top Tech Jobs & Startup Jobs in Dublin

Own fraud detection and prevention for a product by analyzing large datasets, identifying fraud patterns, creating risk rules, optimizing false-positive rates and 3DS decisions, and improving verification workflows. Collaborate with Product, Payments, Data, and Engineering teams while tracking fraud trends and communicating data-driven recommendations. Strong SQL and practical payments-risk expertise are required; Python and fintech experience are preferred.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Analyze payment funnel health across multiple providers by monitoring alerts, authorization rates, decline codes, and conversion. Lead root-cause investigations into technical defects, user behavior, and processor outages; translate findings into actionable insights for cross-functional stakeholders. Build reporting and dashboards, partner with Product, Engineering, Data Science, and Fraud, and develop expertise in payment funnel performance.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and scale backend systems for Eneba’s global gaming marketplace and related products. Develop PHP and Symfony services within a large microservices ecosystem, address performance, reliability, trust, and safety challenges, and contribute to architectural decisions. Collaborate cross-functionally, apply distributed-systems patterns such as CQRS and service orchestration, take ownership of technical outcomes, and remain product-minded while improving the customer experience.

Lead Eneba’s analytics engineering function and a team of approximately 10 engineers. Set strategy, establish data-modeling architecture and quality standards, and deliver trusted, curated data for business teams, BI tools, and AI agents. Partner with senior stakeholders, translate business needs into technical direction, evaluate analytics tooling, promote self-service analytics, and coach team members through hiring, performance management, and professional development.