Software Development Engineer II- Trust Intelligence Platform

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 detection rates and fraud loss rates through the implementation of data and feature flywheels.

Responsibilities

• Ensure data integrity by building the feature anomaly and drift detection capabilities to improve feature quality, and following global financial data privacy standards.
• Design and Implement robust data pipelines using technologies like Kafka, UEL, or Spark to process real-time and batch risk signals.
• Develop Scalable Data Models that support both real-time decisioning and long-term analytical needs, ensuring high data quality and observability.
• Partner with Data Scientists/Analysts to build and optimize "feature stores" and data delivery mechanisms that enable rapid ML model deployment and retraining.
• Contribute to Technical Strategy for the risk data stack, participating in decisions on database selection (SQL/NoSQL), storage patterns, and cost-optimization on AWS.
• Collaborate and Improve the engineering team by sharing best practices for data engineering, participating in design reviews, and promoting a culture of technical excellence.

Requirements

• Experience: 3+ years of professional experience in software engineering, with experience building and maintaining production-grade data systems.
• Technical Depth: Proficiency in Python, Java, Scala, or Go, and hands-on experience with modern big data tools (e.g., Spark, Snowflake, Kafka, or Airflow).
• Cloud Experience: Experience building and scaling distributed data systems within AWS (e.g., Kinesis, S3, EMR, Redshift, or DynamoDB).
• Streaming Knowledge: Experience with, or an understanding of, building low-latency streaming applications for real-time use cases.
• Experience analyzing a problem from different angles: Strong SQL skills and an understanding of data warehousing principles.
• Project Ownership: Experience owning project components or features, seeing them through from design to production.
• Machine Learning: (Preferred) Exposure to using machine learning for feature outlier and drift detection.
• Domain Knowledge: (Bonus) Previous experience in FinTech, Fraud, or Risk domains, specifically dealing with adversarial data patterns or high-volume transaction processing.

Qualifications

• Education: Bachelor's degree in Computer Science, Engineering, or related field.
• Language: Fluency in English.

Skills

• Strong programming skills in Python, Java, Scala, or Go.
• Experience with big data technologies such as Apache Spark, Kafka, and AWS services.
• Knowledge of data warehousing and SQL.
• Experience with data pipeline development and optimization.
• Understanding of cloud computing and distributed systems.
• Experience with machine learning and data analytics.

Benefits

• Flexible paid time off
• Health, dental, and vision + 401k plan with company matching
• Paid parental, medical, military and family care leave
• Mental Health & Family Forming Benefits
• Employee Stock Purchase Plan (ESPP)
• Continuing education and travel benefits

Pay

The starting base salary range for this position is typically $144,000-$180,000. In the U.S., Remitly employees are shareholders in our Company and equity is part of our total compensation plan. Your recruiter can share more information about medical benefits offered, as well as other financial benefits and total compensation components offered with this role.

Schedule

Corporate team members are expected to be in the office at least 50% of the time monthly, typically achieved by coming in three days a week. This creates a consistent, meaningful overlap that supports team norms and business needs. Managers also have the flexibility to set higher expectations based on their team's specific needs.