Senior Real-Time Risk Data Engineer

Remitly, Inc.; Seattle (WA); On-site; USD 180,000 - 225,000; Full time

Benefits offered by this job

Flexible paid time off

Health, dental, and vision + 401k plan

Parental, medical, military and family

Mental Health & Family Forming

Employee Stock Purchase Plan

Education & travel benefits

Job summary

Remitly is seeking a Senior SDE in Trust Intelligence to architect scalable data infrastructure powering risk decisioning and machine learning engines. You will lead high-throughput pipelines and feature stores, enabling rapid ML deployments at a global scale.

Collaborating with Data Scientists, ML Engineers, and Risk Leaders, you will guide architectural decisions, ensure data quality, and mentor engineers across the team.

Qualifications

• 6+ years of professional experience in software engineering.
• Architectural expertise in large-scale distributed systems and trade-offs.
• Proficiency in Python, Java, Scala, or Go and hands-on with Spark, Snowflake, Kafka, Airflow.

Responsibilities

• Architect and scale distributed data systems and pipelines using Kafka and Spark for real-time and batch risk signals.
• Lead the data stack strategy, decisions on SQL/NoSQL, storage, streaming, and AWS cost optimization.
• Design complex data models and feature delivery mechanisms for real-time decisioning and long-term analytics with high data quality.
• Innovate data integrity with feature anomaly and drift detection across global privacy standards.
• Partner with Data Scientists, ML Engineers, and Risk Leadership to build feature stores and ML deployment infrastructure.
• Mentor mid-level and junior engineers, lead architecture reviews and set engineering standards.

Skills

Python

Java

Scala

Go

Tools

Spark

Snowflake

Kafka

Airflow

Kinesis

S3

EMR

Redshift

DynamoDB

Job description

Remitly is seeking a Senior SDE in Trust Intelligence to architect scalable data infrastructure powering risk decisioning and machine learning engines. You will lead high-throughput pipelines and feature stores, enabling rapid ML deployments at a global scale.

Collaborating with Data Scientists, ML Engineers, and Risk Leaders, you will guide architectural decisions, ensure data quality, and mentor engineers across the team.