Senior Software Development Engineer- Trust Intelligence

Job Description:

At Remitly, we believe everyone deserves the freedom to access, move, and manage their money wherever life takes them. Since 2011, we've tirelessly delivered on our promise to customers sending money globally, providing secure, simple, and reliable ways to manage their money, ensuring true peace of mind. Whether it's supporting loved ones back home, growing a business across continents, or pursuing new opportunities abroad, we're not just here to move money—we're here to move our global customers forward.
We're looking for builders, reimaginers, and global thinkers who want to work at the intersection of technology, trust, and transformation. If that's you and you're ready to do the most meaningful work of your career—we invite you to join over 2,800 passionate Remitlians worldwide who are united by our vision to transform lives with trusted financial services that transcend borders.

About the Role

The Trust Intelligence Platform team provides a robust data foundation and high-quality risk signal intelligence as its top priority. The team converts raw platform data into clean, reliable, and contextualized intelligence for downstream use in models, rules, and policies. This enables improvements in transaction defection rates and fraud loss rates through the implementation of data and feature flywheels.

As a Senior SDE in Trust Intelligence, your mission is to architect the future of the data infrastructure powering our risk decisioning and machine learning engines. You will lead the technical vision for building and scaling high-throughput, fault-tolerant pipelines and next-generation feature stores. You will drive initiatives to build intelligence through first-party and third-party data sources, and enable feature automation and lifecycle management. Ultimately, as a senior member of the team, your architectural decisions will empower our fraud analysts and machine learning engineering teams to combat risk at a global scale with absolute confidence.

You Will

• Architect and Scale distributed data systems and robust pipelines using technologies like Kafka, and Spark to process high-throughput, low-latency real-time and batch risk signals.

• Drive Technical Strategy for the risk data stack, leading decisions on database selection (SQL/NoSQL), storage patterns, streaming architecture, and large-scale cost-optimization on AWS.

• Lead the Design of complex data models and feature delivery mechanisms that support both real-time decisioning and long-term analytical needs, ensuring uncompromising data quality and observability.

• Innovate Data Integrity by defining the roadmap and building advanced feature anomaly and drift detection capabilities, ensuring strict adherence to global financial data privacy standards.

• Partner and Align cross-functionally with Data Scientists, ML Engineers, and Risk Leadership to build out "feature stores" and infrastructure that enable rapid ML model deployment and automated retraining loops.

• Act as a Force Multiplier by mentoring mid-level and junior engineers, leading architectural design reviews, setting engineering standards, and fostering a culture of technical excellence across the platform.

You Have

• Experience: 6+ years of professional experience in software engineering, with a proven track record of architecting, building, and maintaining production-grade, highly scalable data systems.

• Architectural Expertise: Proven ability to design large-scale, distributed systems and make high-stakes architectural trade-offs. Deep understanding of data warehousing principles and modern data lake architectures.

• Technical Depth: Expert-level proficiency in Python, Java, Scala, or Go, and extensive hands-on experience with modern big data tools (e.g., Spark, Snowflake, Kafka, Airflow).

• Cloud & Streaming Mastery: Extensive experience building, scaling, and cost-optimizing distributed data systems within AWS (e.g., Kinesis, S3, EMR, Redshift, DynamoDB). Proven experience with low-latency streaming applications for real-time use cases.

• Technical Leadership: Demonstrated experience leading large technical initiatives across multiple quarters, aligning cross-functional teams, and seeing complex systems through from conception to production and deprecation.

• Machine Learning Integration: (Preferred) Experience designing the data infrastructure that directly serves machine learning pipelines, particularly involving feature outlier and drift detection methodologies.

• Domain Knowledge: (P