Your Role Involves:

• System Development at Scale Designing, developing, and maintaining high-scale data-driven systems
• Implementing optimizations and monitoring strategies to ensure performance and reliability
• Data Pipeline Solutions Architecting, building, and troubleshooting end-to-end data pipelines
• Collaborative Growth Working closely with data scientists and analysts to develop and refine threat intelligence solutions
• Participating in code reviews and knowledge-sharing sessions to continuously improve team output
• Machine Learning Pipelines Building and managing ML pipelines for training, deployment, and serving various model types (e.g., supervised, unsupervised)
• Ensuring model performance, scalability, and integration with broader data infrastructure
• Microservices Architecture Developing, testing, and maintaining microservices-based systems that handle data and ML workflows
• Ensuring robust, scalable, and secure service designs using industry best practices
• Tooling Staying updated on emerging technologies and frameworks to continuously improve the development process

What You Need:

• 5+ Years experience in software development and/or data engineering
• Experience in Big Data Solutions
• Proven track record designing, building, and maintaining large-scale, distributed data systems.
• Familiarity with data processing frameworks and architectures (e.g. Kafka, Temporal, Spark)
• Microservices Expertise
• Hands-on experience building and testing microservices systems (e.g., Docker, Kubernetes, ArgoCD)
• Solid understanding of service-to-service communication, APIs, and security best practices
• Proficiency in relevant programming languages (e.g., Python, Go, Java/Scala)
• Strong Communication and Collaboration Skills
• Proven ability to work effectively with cross-functional teams, including data scientists, analysts, and stakeholders
• Familiarity with Industry Best Practices
• Experience working with CI/CD pipelines, automation tools, and DevOps methodologies
• Leveraging tools like ArgoCD, PostgreSQL, Grafana and AtlasGo, to maintain best-in-class DevOps practices