Contributes to software engineering communities of practice and events that explore new and emerging technologies
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Adds to team culture of diversity, equity, inclusion, and respect
Detects, troubleshoots and Resolves Production issues in an Enterprise multi-environment settings using advanced observability tools
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Proficient in coding in one or more languages - Java, Python
Proficient in private and public Cloud hosted Platform - Pivotal Cloud, AWS. In-depth knowledge is a must for AWS ECS, S3, SQS, SNS, Lambda, Glue, pySpark, cloudwatch
Experienced in SRE and Observability tools - Splunk, Dynatrace and Datadog
Proficient in Oracle, DB2 and AW Aurora Postgress Database. Hands on experience writing SQL is a must
Hands on experience in Software Development tools and CI/CD tools - Jenkins, Spinnaker, IntelliJ, Bitbucket
Hands on experience in IAC - Terraform
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Preferred qualifications, capabilities, and skills
Overall knowledge of the Software Development Life Cycle
Familiarity with modern front-end technologies
Experience in Data Pipeline design and development
Experience in Cloud migration projects
Experience in AWS Glue and PySPark
