Develop reusable frameworks, utilities, templates, and self-service capabilities that reduce duplication and improve developer productivity.
Write comprehensive unit, integration, and regression tests using appropriate mocking, fixtures, automation, and code-coverage practices.
Diagnose software defects, runtime failures, and performance bottlenecks; conduct root-cause analysis and implement sustainable remediation.
Design, configure, deploy, and support containerized applications on Kubernetes in AWS, Azure, or comparable public-cloud environments.
Use Infrastructure as Code tools such as Terraform, AWS CloudFormation, or Azure Resource Manager templates to automate cloud and platform infrastructure.
Build and maintain CI/CD workflows using technologies such as GitHub, Jenkins, and Artifactory for source control, testing, packaging, artifact management, deployment, and release automation.
Implement structured logging, metrics, dashboards, monitors, and alerts to support highly available and operationally reliable applications and platforms.
Apply secure software-development practices, including peer reviews, dependency management, vulnerability remediation, secrets protection, and secure cloud configuration.
Apply relational database concepts and SQL skills when designing application persistence, integrations, or data-processing solutions.
Participate in technical design reviews, architecture discussions, code reviews, and the definition and adoption of engineering standards.
Collaborate with software engineers, platform engineers, architects, support teams, and business stakeholders to translate evolving requirements into effective software solutions.
Produce technical designs, application documentation, operational procedures, troubleshooting guides, and root-cause analysis records.
Use AI-assisted development tools, including coding copilots and large language models, to improve development, testing, documentation, code review, and troubleshooting.
Experience Requirements
Experience developing enterprise Python applications, backend services, API integrations, automation tools, internal frameworks, or platform-engineering software is preferred.
Experience with Kubernetes troubleshooting, cloud-platform engineering, high-availability design, application performance testing, and enterprise observability platforms is preferred.
Familiarity with ETL/ELT pipelines, Apache Airflow, Apache Spark, and distributed data-processing concepts is beneficial.
AWS or Azure cloud certification is a plus.
