AWS Expertise: Proficient in utilizing AWS services, including VPC, EC2, EFS, Auto Scaling, Elastic Load Balancer, IAM, CloudWatch, S3, SNS, Lambda, Secrets Manager, Elastic Cache, Security Hub, SES, S3 Glacier, and RDS. Infrastructure as Code (IaC): Skilled in creating AWS resources using CDK or Terraform, ensuring consistent and repeatable infrastructure deployments. CI/CD Pipeline Management: Experienced in configuring Jenkins from scratch, integrating CI/CD pipelines with SAST tools like SonarQube, and managing artifact repositories using JFrog. Containerization (Docker, Kubernetes, AWS Fargate): Critical for developing, deploying, and managing containerized applications. Database Setup and Management: Competent in setting up and managing databases such as MySQL, MongoDB, and Elasticsearch from the ground up. Experience setting up Kafka, Debezium, and CDC pipelines to build data lakes. Reverse Proxy Management: Expertise in configuring and managing NGINX and HAProxy. Monitoring and Logging: Capable of configuring AWS CloudWatch, setting up Nagios for server and service health checks. Experience with setting up ELK for application log monitoring. Scripting & Automation: Adept at writing shell scripts for OS and application automation tasks. Environment Management: Managing development, QA, UAT, pre-production, and production environments, designing instance strategies for various releases. Collaboration: Working closely with software developers, QA, and IT teams to debug build failures and resolve application and infrastructure issues.
Develop, optimize, and maintain scalable applications for web and mobile platforms using React and React Native. Lead feature development with an emphasis on quality, security, and performance. Collaborate with cross-functional teams, including design and product, to deliver new features and enhancements. Mentor and guide junior engineers, sharing best practices and promoting a high standard of coding. Actively participate in code reviews, ensuring adherence to best practices and high code quality.
We are looking for a passionate analytics professional with a knack for seeing solutions in sprawling data sets and the business mindset to convert insights into strategic insights. You will be responsible for, Identify various factors and design key metrics and maintain various dashboards Interpret data, analyse results using statistical techniques and present results effectively to key stakeholders. Analyse data from multiple sources to identify discrepancies, spot patterns, and eliminate suspicion Execute quantitative analysis that translates data into actionable insights Build tools internally for operational optimisation and improve efficiency Technical Skills and Experience 1-2 years of experience in business intelligence and analytics Strong Analytical aptitude and logical reasoning ability Mandatory Skills : Python , SQL/MySQL, Excel Optional Skills: Power BI, Superset or other BI tools Experience in handling complex data sources Basic knowledge of supervised and unsupervised machine learning algorithms Preferably worked in an in-house analytics team for a digital lending firm or projects. Experienced in risk management of different lending products across the lifecycle of consumer credit Technology-driven mindset, digitally curious, up-to-date with digital and technology literature, trends Excellent communication, story-telling and presentation skills
Develop high-quality, maintainable backend systems Optimize performance and scalability in a microservice environment Collaborate with cross-functional teams to deliver solutions Implement robust testing practices (TDD) Leverage technologies like MySQL, MongoDB, Redis, and Kafka/RabbitMQ Participate in code reviews and maintain documentation Qualifications: 2+ years in backend development Strong understanding of data structures, algorithms, and database systems Experience with AWS and DevOps basics
What You ll Do Fraud Detection & Prevention Assist in analyzing UPI and QR transaction data to detect suspicious patterns. Contribute to rule-based and ML-assisted models to identify abnormal payment behavior. Support device fingerprinting, location-based risk analysis, and velocity checks. Work with the engineering team to help integrate basic fraud detection rules into payment systems. Risk Analytics & Model Development Work alongside senior analysts to build and test supervised/unsupervised ML models. Explore trends and anomalies in transaction data such as high-frequency usage or mismatches in device/location. Help in monitoring model performance and fine-tuning risk thresholds. Regulatory Risk Support Learn to interpret and apply NPCI and RBI fraud risk guidelines to support internal controls. Participate in tracking fraud cases and assist with reporting and root cause analysis. Cross-Team Collaboration Collaborate with product, tech, and compliance teams to support fraud mitigation strategies. Share data insights that help improve transaction safety and customer trust. What We re Looking For Experience: ~1.5 years in fraud analytics, data science, or risk analysis in UPI or digital payments. Technical Skills: Good hands-on knowledge of Python and SQL. Familiarity with tools like Pandas, scikit-learn, or similar. Analytical Thinking: Eagerness to solve problems using data and uncover patterns. Learning Mindset: Willingness to learn new tools and techniques under the guidance of senior team members. Team Player: Strong communication and collaboration skills. Why Join Zype Start Strong: Build your career in a fast-growing fintech working on real-time payment risk. Mentorship & Learning: Get hands-on mentorship from experienced fraud analysts and data scientists. Tech Exposure: Work with real-world data, modern AI tools, and scalable systems. Purpose-Driven Work: Help build safer, more secure digital payment systems for millions of users. Excited to dive into fraud analyticsCome grow with Zype!
Data Understanding and Modelling: Investigate, review, and analyze structured and unstructured data sources to build an in-depth understanding of the underlying data and business domain. Design and create common/master data models based on this understanding. Architectural Design and Implementation: Collaborate with leadership to design and implement data engineering architecture, ensuring high availability, scalability, and reliability of systems. Data Ingestion and ETL Development: Lead the development of a data lake by ingesting data from various sources. Build scalable and reliable ETL pipelines and processes to handle a diverse range of data sources. Mission-Critical Systems Development: Design and develop systems with a focus on high availability and performance to meet critical business needs. Data Performance and Availability: Ensure high performance and availability of data for analytics and data science teams. Collaboration and Technical Solutions: Work closely with technical engineers and architects to deliver appropriate technical solutions aligned with business requirements. Data Security and Infrastructure Ownership: Take ownership of data security measures for the organization. Forecast and manage data infrastructure needs. Adaptability and Startup Environment: Display a self-motivated, adaptable, and driven mentality, thriving in a fast-paced startup environment.