Quick overview
Job description
- Location: Remote
- Department: Data Delivery and Governance
- Schedule: Days | Full-Time
- Eligible for an annual bonus incentive
#LI-Remote
Team Leadership: Manage, mentor, and grow a high-performing, diverse team of data engineers, DevOps specialists, and platform engineers.
Operational Excellence: Own the reliability, high availability, and operational health of mission-critical, enterprise-level production data systems.
Cloud Architecture & Delivery: Oversee the architectural design and deployment of scalable data solutions across the Google Cloud Platform (GCP) ecosystem.
Process Automation: Lead the strategy and execution for CI/CD pipelines, Infrastructure as Code (IaC), and robust observability frameworks.
AI Innovation: Champion and integrate AI-assisted engineering practices to optimize development workflows, boost efficiency, and improve system resilience.
Cross-Functional Collaboration: Partner closely with product, analytics, and business teams to align technical deliverables with strategic goals, leveraging enterprise operational and analytics tools.
Education:
- High School diploma equivalency with 3 years of cumulative experience OR Associate's
- degree/Bachelor's degree with 2 years of cumulative experience OR 7 years of applicable cumulative job specific experience required.
Additional Preferences:
Domain Experience: 8+ years of hands-on experience in Cloud Data Engineering, DevOps, Software Engineering, Platform Engineering, or Data Operations.
Leadership: 3+ years of technical leadership experience actively managing engineering or operational teams.
Production Operations: Proven track record of managing enterprise-level production systems with stringent high-availability and operational excellence requirements.
GCP Ecosystem: Deep technical expertise in core Google Cloud Platform services, specifically BigQuery, Cloud Composer/Airflow, Dataflow, Dataproc, and Cloud Storage.
Automation & CI/CD: Strong proficiency with Infrastructure as Code (IaC), CI/CD pipelines, automated testing, and modern observability tooling.
Enterprise Tooling: Hands-on experience with enterprise operational and project management tools (ServiceNow, Jira, Confluence) as well as BI/analytics platforms (Power BI, Looker).
Engineering Culture: Active interest in AI-assisted engineering practices and a strong commitment to cultivating an inclusive, collaborative, and high-performing team culture.