The Fraud Detection team is looking for a strong Senior Backend Engineer to help scale and evolve our industry-leading real-time fraud prevention systems.
We develop high-throughput, low-latency services that power real-time decision-making at scale. Our platform combines modern backend engineering with advanced streaming, big data technologies, and AI-powered models to protect businesses and their users from fraudinstantly and effectively.
This role is critical in delivering scalable backend infrastructure to power our machine learning-driven fraud detection capabilities.
What youll do:
Design and Build Scalable Backend Services:
Design and implement real-time streaming applications for processing high-throughput data using Kafka and gRPC.
Develop scalable and reliable streaming solutions for fraud detection and prevention.
Develop and Optimize ETLs:
Build, and maintain batch pipelines for data enrichment, normalization, and aggregation using tools like Spark and BigQuery.
Ensure data pipelines are robust, scalable, and cost-efficient.
Support Data Science and ML-Ops:
Collaborate with Data Science teams to operationalize machine learning models in real-time streaming environments and batch ETLs.
Focus on Performance and Resilience:
Continuously improve the throughput, latency, and fault tolerance of critical services operating under high load and real-time constraints.
Collaborate Across Teams:
Work closely with Fraud Data Scientists, and Product Managers to deliver impactful solutions.
Partner with stakeholders to understand the business impact of fraud detection and align technical solutions with business needs.
Innovate and Improve:
Continuously improve the scalability and performance of existing systems.
Explore and integrate new tools and technologies to enhance streaming and batch-processing capabilities.
Requirements: What you have
Backend Engineering Expertise:
5+ years of experience designing and building scalable, resilient backend systems with a strong focus on performance, reliability, and clean architecture.
Distributed Systems & Scalability:
Hands-on experience with high-throughput, low-latency, and mission-critical distributed systems operating at scale.
Strong Big Data Foundations:
Deep understanding of data pipelines and distributed processing frameworks such as Spark and BigQuery, with a focus on performance, efficiency, and cost optimization.
Data Science and ML Collaboration:
Exposure to machine learning workflows, including data preparation, model deployment, and monitoring.
Collaboration & Ownership:
A strong sense of ownership, paired with excellent collaboration skills, to work effectively across engineering, product, and data teams.
This position is open to all candidates.