Principal Software Engineer

Job Summary:

We are looking for an experienced Principal Engineer to lead Risk Analytics, Digital Experience Monitoring (DEM), and Data Engineering initiatives. This role requires a deep understanding of data analytics, system observability, risk management solutions, and backend engineering. The ideal candidate should have strong expertise in Golang, Java, AWS cloud services (Kinesis, Redshift, Elasticsearch), and exposure to DEM tools. The candidate will play a strategic role in designing scalable data solutions that drive risk intelligence, operational efficiency, and digital experience monitoring.

Key Responsibilities:

• Lead the design and development of Risk Analytics and DEM solutions to monitor system performance, user experience, and operational risks.
• Architect and optimize scalable data pipelines and analytics applications leveraging AWS services (Kafka, Redshift, Elasticsearch, S3, Lambda, etc.).
• Develop real-time and batch processing applications using Golang and Java to support risk assessment and predictive analytics.
• Implement observability and monitoring frameworks to enhance system resilience.
• Collaborate with data scientists, risk analysts, and engineering teams to develop data-driven decision-making frameworks.
• Ensure data compliance, security, and governance across all analytics platforms.
• Optimize data ingestion, storage, and retrieval mechanisms for high availability and low-latency insights.
• Conduct technical mentorship to improve engineering best practices.
• Stay ahead of emerging trends in risk analytics, AI/ML-driven monitoring, and cloud-native solutions.

Required Qualifications:

• 12+ years of experience in software development, data analytics, risk intelligence, and monitoring solutions.
• Expertise in Golang and Java for backend development and data processing.
• Strong hands-on experience with AWS cloud services (Kafka, Redshift, Elasticsearch, Glue, S3, Lambda).
• Experience in Digital Experience Monitoring.
• Knowledge of big data processing frameworks (Spark, Flink).
• Proven experience in risk analytics, compliance monitoring, or fraud detection.
• Strong understanding of observability solutions, Application Performance Monitoring, and log monitoring.
• Experience with real-time streaming architectures (Kafka, AWS Kinesis).
• Proficiency in SQL, NoSQL databases, and data modeling.
• Strong problem-solving, and communication skills.

Preferred Qualifications:

• Experience in AI/ML for predictive risk analytics.
• Expertise in containerized and serverless architectures (Docker, Kubernetes, AWS Lambda).
• Familiarity with CI/CD automation, DevOps, and Infrastructure as Code (IaC).
• Exposure to cybersecurity risk assessment and compliance frameworks.