WHY SOCURE?

At Socure, we’re on a mission—to verify 100% of good identities in real time and eliminate identity fraud from the internet.
Using predictive analytics and advanced machine learning trained on billions of signals to power RiskOS, Socure has created the most accurate identity verification and fraud prevention platform in the world. Trusted by thousands of leading organizations—from top banks and fintechs to government agencies—we solve real, high-impact problems at scale. Come join us!

PREFERRED QUALIFICATIONS

• Experience building velocity systems and/or behavioral signal aggregators.
• Familiarity with real-time analytics or OLAP engines such as Druid, ClickHouse, or in-memory stores like Redis.
• Exposure to data quality frameworks, testing strategies, and pipeline observability tools.
• Experience working in fraud detection, risk scoring, or identity verification domains.
• Strong system design and infrastructure optimization skills in high-scale environments.
  Socure is an equal opportunity employer and values diversity of all kinds at our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

ABOUT THE ROLE

Socure is seeking a talented and experienced Senior Backend Engineer to join our Digital Intelligence team. This role involves building data platforms and real-time systems that process large-scale device and behavioral data used in identity verification and fraud prevention. You will be responsible for the design and optimization of batch and streaming pipelines, with a focus on velocity computation and real-time aggregation frameworks.
This role is dedicated to the Digital Intelligence team and will support our engineers and data scientists by ensuring data is clean, accessible, and ready for analysis, modeling, and real-time decision-making.

WHAT YOU’LL DO:

• Design, build, and maintain scalable data pipelines to collect, clean, transform, and aggregate large-scale device and behavioral signals.
• Build and support real-time velocity systems that compute session-level and user-level aggregations with sub-second latency.
• Develop and manage data infrastructure such as data lakes and data warehouses.
• Optimize data architectures for performance, scalability, cost efficiency, and maintainability.
• Collaborate with ML engineers, data scientists, and product stakeholders to ensure data availability, reliability, and privacy compliance.
• Maintain and enhance orchestration workflows; automate workflows and monitoring systems.
• Ensure data quality, consistency, and security across systems; implement best practices in data modeling, schema evolution, and pipeline observability.
• Document systems and processes for internal use and compliance.