Senior Software Engineer, Identity Intelligence

Ukraine

Auto‑apply with AI Apply

Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Cybersecurity, Fraud Prevention, Data & Analytics, Cloud ComputingIndustries

Requirements

Candidates should possess 7+ years of experience building distributed backend systems in Java, 3+ years designing and delivering highly available services, and a strong foundation in data structures, distributed algorithms, and async architectures. They should have hands-on experience with cloud platforms (AWS/GCP), deployment, and infrastructure management, as well as expertise with large-scale NoSQL databases such as HBase, Cassandra, or Bigtable. A Bachelor’s degree in Computer Science, Mathematics, or Engineering, or equivalent practical experience is required.

Responsibilities

As a Senior Software Engineer, you will build highly scalable, distributed services to handle hundreds of millions of events per day, partner with product management to scope and shape project requirements, implement engineering solutions to address complex customer needs at scale, collaborate with other engineers within the team and across other engineering teams, document and demonstrate solutions by developing documentation and code comments, and help evolve and improve engineering practices.

Skills

Java

Apache Flink

GCP

Dataflow

Spanner

BigTable

BigQuery

Kafka

DropWizard

gRPC

Snowflake

Distributed Systems

Concurrency

Scalability

Data Engineering

API Development

Sift

Real-time fraud detection and prevention platform

About Sift

Sift provides a platform focused on detecting and preventing online fraud in real-time, catering to clients in e-commerce, fintech, and digital marketplaces. The platform uses machine learning and artificial intelligence to analyze large datasets, allowing it to identify fraudulent activities effectively. One of its standout features is dynamic friction, which ensures that genuine users have a smooth experience while preventing fraudsters from accessing services. Sift's business model is subscription-based, with fees that depend on transaction volume and service level. Additionally, Sift offers services like chargeback management and dispute resolution, which add further value to its offerings. The company's goal is to enhance digital trust and safety for businesses by providing tools that help them make informed decisions and protect against fraud.

Bristol, United KingdomHeadquarters

2011Year Founded

$4.4MTotal Funding

SERIES_ACompany Stage

AI & Machine Learning, Financial ServicesIndustries

51-200Employees

Risks

Rise of app-enabled friendly fraud challenges Sift's mobile fraud detection capabilities.

Reliance on third-party delivery apps by QSRs introduces new fraud risks for Sift.

Complex payment processes may complicate Sift's integration and effectiveness in fraud prevention.

Differentiation

Sift offers a comprehensive platform for real-time online fraud detection and prevention.

The company uses machine learning and AI to analyze vast amounts of data effectively.

Sift's dynamic friction feature ensures seamless user experience while blocking fraudsters.

Upsides

Growing demand for AI-driven fraud detection in QSRs presents expansion opportunities for Sift.

Digital-first banks' need for effective authentication aligns with Sift's fraud prevention solutions.

Global trend towards secure payment systems supports Sift's mission for digital trust and safety.

Land your dream remote job 3x faster with AI

Try Jobo Free