Leads and mentors engineers and partners with product, analytics, and security teams to deliver roadmaps and execution.
Uses enterprise-authorized GenAI developer tools such as GitHub Copilot and Claude Code to accelerate refactoring, test generation, and documentation with strong validation and controls.
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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Required qualifications, capabilities, and skills
Formal training or certification on data engineering concepts with architecture and production ownership concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Strong Python coding skills, and deep experience with Databricks and Spark.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Strong experience with AWS and modern lakehouse and data platform patterns.
Proven technical leadership through architecture decisions, mentoring, and cross-team influence.
Preferred qualifications, capabilities, and skills
AWS certification and/or Databricks certification.
Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery.
