Lead Software Engineer

Job Summary (List Format): - Establish and enforce best practices for microservices architecture to ensure scalability, reliability, and maintainability. - Collaborate with cross-functional teams to gather project requirements and deliver scalable solutions. - Mentor and guide team members on microservices design patterns, principles, and technologies. - Design, develop, and maintain microservices, primarily using Golang. - Continuously improve services by fixing bugs, enhancing performance, and adding new features. - Containerize microservices and prepare deployments for Kubernetes environments using Helm charts. - Ensure the resilience and reliability of deployed services. - Gain a comprehensive understanding of the end-to-end technical architecture and apply this knowledge in development. - Develop and maintain high-coverage automated tests (unit, contract, integration, e2e). - Manage source code using Git and handle build, test, and deployment processes with CI/CD pipelines. - Build and test solutions remotely, deploying to lower-level environments as needed. - Conduct code reviews to identify errors and improve code quality, performance, and readability. - Lead design and process discussions, driving technical decisions within the team. - Report project status and manage risks for assigned applications/services. - Participate in demos and acceptance discussions with stakeholders. - Assist in sprint-based task planning, estimation, and review. - Own the delivery of assigned tasks, including design, development, testing, deployment, configuration, and documentation. - Collaborate with data science teams to implement and engineer features based on their requirements. - Bridge the gap between architecture and engineering, aligning development with architectural guidance. - Write high-quality production code in Python/Golang and SQL; be open to learning new languages and frameworks. - Work with large data volumes and build high-throughput, low-latency solutions using Kafka and Kubernetes. - Apply modern service design approaches, leveraging containers, orchestration tools, APIs, and NoSQL/in-memory data stores. - Resolve technical challenges by selecting pragmatic or rigorous solutions as appropriate. - Follow devops-based workflows, contributing to continuous integration and deployment processes. - Communicate effectively with various stakeholders, including engineers, analysts, devops, security, and data science professionals. - Adhere to code review, version control, and change control best practices, balancing quality and scalability. - Optimize solution performance within given technology constraints. - Engage with or show willingness to productionize machine learning solutions and large-scale feature engineering.