Senior Software Engineer

Summary

Join the Identity Protection team at Sift and build Account Defence, a real-time solution safeguarding user identities. As a Senior Software Engineer, you will collaborate with various teams to develop and deliver key Identity Protection features. You will build highly scalable, distributed services, partner with product management, design and implement engineering solutions, collaborate across teams, and contribute to improving engineering practices. The ideal candidate possesses 7+ years of experience building distributed backend systems using Java and 3+ years of experience with cloud infrastructure. Strong software engineering fundamentals, database knowledge, and excellent communication skills are essential. Sift is an AI-powered fraud platform securing digital trust for leading global businesses.

Requirements

• 7+ years of experience building distributed backend systems using Java
• 3+ years of experience with cloud infrastructure (e.g., GCP, AWS, Azure)
• Strong software engineering fundamentals, including data structures, algorithms, and distributed systems, with excellent debugging, testing, and problem-solving skills
• Solid understanding of relational and NoSQL database modeling and design
• Excellent communication and collaboration skills, valuing team success above individual accomplishments
• A self-starter attitude with a quick learning curve

Responsibilities

• Build highly scalable, distributed services capable of processing hundreds of millions of events per day
• Partner with product management to define and scope project requirements
• Design and implement engineering solutions to solve complex customer challenges at scale
• Collaborate across engineering teams to deliver reliable and efficient products
• Contribute to evolving and improving engineering practices within the team

Preferred Qualifications

• Experience with stream processing frameworks such as Apache Flink, Apache Beam, or Dataflow
• Familiarity with HBase, BigTable, Kafka, ZooKeeper
• Experience with practical challenges in ML systems, including feature extraction and definition, data validation, training, monitoring, and management of features and models