Monitors and responds to production alerts, providing L1 and L2 support to minimize customer impact.
Provides weekend and on-call production support as part of the team rotation.
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.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience.
Strong analysis, research, investigation, and evaluation skills, with a structured approach to problem solving.
Proficient in at least one programming language such as Python, Java/Spring Boot, Scala, and/or .Net.
Hands-on with infrastructure as code (IaC) using tools such as Terraform and AWS CDK.
Experience developing and running data pipelines using PySpark and good debugging skills.
Experience managing and developing/deploying on Cloud (private cloud or public cloud).
Working knowledge of Kubernetes, Docker, and other container technologies.
Experience with monitoring and observability tools, including Dynatrace, Open Telemetry (OTEL), Prometheus, Datadog, and Grafana, particularly in dashboard development.
Knowledge of GIT, Bitbucket, Jenkins, SONAR, SPLUNK, Maven, AIM and Continuous Delivery tools.
Demonstrated experience using and leading effective use of enterprise-authorized, AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) to support SRE workflows — with strong validation habits and the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
Preferred qualifications, capabilities, and skills
Production working knowledge of Databricks and Apache Airflow on AWS.
AWS, Databricks, and/or Terraform certification.
Working knowledge of Control-M/Autosys, Airflow scheduling package.
UNIX file management & administration and good shell scripting experience.
Oracle (v9i/10/11/19c) running on Exadata, Ansi SQL, PL/SQL Stored Procedures support/development.
