Software Development Engineer II- Trust Intelligence Platform
About The Position
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. As an SDE II in Trust Intelligence, your mission is to guarantee the quality and integrity of the data powering our risk decisioning and machine learning engines. You will design, build, and scale the high-throughput pipelines and feature stores that our decisioning logic relies upon. Through rigorous data quality automation—including real-time anomaly monitoring and statistical drift detection—you will establish a definitive source of truth. Your infrastructure will empower our fraud analysts and machine learning engineering teams to combat risk with absolute confidence. You will report to an Engineering Manager in this role.
Requirements
- 3+ years of professional experience in software engineering, with experience building and maintaining production-grade data systems.
- Proficiency in Python, Java, Scala, or Go, and hands-on experience with modern big data tools (e.g., Spark, Snowflake, Kafka, or Airflow).
- Experience building and scaling distributed data systems within AWS (e.g., Kinesis, S3, EMR, Redshift, or DynamoDB).
- Experience with, or an understanding of, building low-latency streaming applications for real-time use cases.
- Strong SQL skills and an understanding of data warehousing principles.
- Experience owning project components or features, seeing them through from design to production.
Nice To Haves
- Exposure to using machine learning for feature outlier and drift detection.
- Previous experience in FinTech, Fraud, or Risk domains, specifically dealing with adversarial data patterns or high-volume transaction processing.
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.
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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
