Sr Manager - Cloud Engineer Senior

Your Opportunity

Schwab remains committed to providing increased visibility to career growth opportunities and job requirements. This posting announcement is part of increased transparency and while all qualified applicants will be reviewed and considered, this organization has a preferred candidate identified for this role. 

At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together. 

We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).

Schwab Technology Services enables the future of how clients manage their money by providing innovative and reliable technology products and services as part of our ongoing commitment to democratize access to investing and financial planning.

Schwab is continuing to scale the Schwab Google Data & Analytics platform ecosystem, which provides secure, reliable, and governed Google Cloud-based capabilities for enterprise data, analytics, predictive modeling, and emerging AI/ML use cases. This senior individual contributor role will focus primarily on Cloud Data Warehouse Platform (CDWP) and PAML (Predictive Analytics machine Learning), two critical platforms that support analytic workloads, data science enablement, predictive model execution, and broader enterprise adoption across Schwab.

We are seeking a senior hands-on cloud platform engineer to strengthen in-house expertise across Google Cloud, data platform services, streaming and real-time patterns, Infrastructure-as-Code, resiliency, tenant enablement, and production operations. This role will help engineer reusable platform capabilities, improve operational reliability, support ongoing disaster recovery exercises and refinements, and reduce dependency on a small number of specialized engineers as SGDA platforms continue to scale.

This is an opportunity to work on enterprise-scale cloud platforms that enable critical data, analytics, and AI/ML capabilities in a highly regulated financial services environment.

In this role you will:

SGDA Platform Engineering

• Engineer, enhance, and support core capabilities across the Schwab Google Data & Analytics platform ecosystem, with primary focus on CDWP and PAML.
• Design and implement scalable Google Cloud platform solutions that support tenant onboarding, workload growth, platform modernization, and new enterprise data and AI/ML use cases.
• Strengthen PAML platform capabilities as adoption expands across predictive modeling, Fraud Analytics, Risk, and other business-aligned analytics organizations.
• Support CDWP platform growth through infrastructure enhancements, service enablement, onboarding improvements, automation, reliability engineering, and operational readiness.
• Build reusable platform patterns that improve consistency, reliability, security, and speed of delivery across

SGDA platforms.

• Google Cloud, Data, and AI/ML Platform Enablement
• Engineer platform capabilities using Google Cloud services that support enterprise data warehousing, distributed processing, orchestration, streaming, event-driven processing, model execution, identity and access controls, networking, and observability.
• Support platform capabilities that interact with ETL, data integration, BI/reporting, analytics, data science, and downstream data consumption patterns.
• Enable scalable and governed platform patterns for analytics users, data science teams, application teams, and business-aligned technology organizations.
• Evaluate and operationalize new Google Cloud services and patterns that improve scalability, reliability, cost efficiency, security, and developer productivity.
• Partner with data, analytics, AI/ML, architecture, Cloud Services, cybersecurity, and application teams to align platform capabilities with enterprise needs.

Infrastructure-as-Code and Automation

• Develop, enhance, and maintain Infrastructure-as-Code patterns, preferably using Terraform, to support secure and repeatable cloud platform deployments.
• Build reusable modules, environment provisioning patterns, access patterns, configuration management, lifecycle changes, and controlled production deployment practices.
• Improve automation across platform onboarding, service enablement, configuration changes, operational tasks, and recurring platform maintenance. 
• Support CI/CD, release automation, version control, and deployment practices that improve delivery quality and reduce manual effort.
• Identify opportunities to simplify platform operations through automation, standardization, and repeatable engineering practices.

Production Reliability, Resiliency, and Operations

• Support production platform services with expectations for availability, incident response, change control, observability, resiliency testing, and operational readiness.
• Drive ongoing resiliency engineering across SGDA platforms by supporting regular disaster recovery exercises, refining recovery procedures, validating platform readiness, and improving operational response for CDWP and PAML workloads.
• Improve monitoring, alerting, dashboards, operational metrics, and support processes to proactively identify issues and strengthen platform reliability.

Troubleshoot complex issues across cloud infrastructure, data platform services, access controls, networking, runtime environments, integration patterns, and production support scenarios.

• Partner with production support, operations, cybersecurity, and application teams to manage platform incidents, risks, lifecycle changes, and continuous improvement actions. Technical Leadership and Cross-Team Collaboration • Provide senior individual contributor leadership by influencing technical direction, improving engineering standards, mentoring engineers, and guiding complex design decisions.
• Translate complex cloud platform topics into clear recommendations for engineering teams, architecture partners, operations teams, and leadership stakeholders.
• Collaborate across SGDA, Cloud Services, Enterprise Architecture, cybersecurity, application teams, and business-aligned technology organizations to deliver secure, reliable, and scalable platform capabilities.
• Identify systemic gaps across platform engineering, support, resiliency, security, and automation practices and lead improvements through influence and technical ownership.
• Contribute to roadmap planning, dependency management, platform modernization, and delivery of high-impact engineering initiatives.

What you have

To ensure that we have fulfilled our promise of "challenging the status quo," this role has specific qualifications that successful candidates should have.

Required: 

• Bachelor degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
• 8+ years of experience in cloud platform engineering, infrastructure engineering, software engineering, data platform engineering, or enterprise application engineering.
• 5+ years of experience in an enterprise data platform ecosystem, including hands-on interaction with ETL, data integration, BI/reporting, analytics, and downstream data consumption patterns.
• 4+ years of hands-on experience designing, building, and operating enterprise-scale cloud platforms, shared platform services, or reusable engineering capabilities.
• 3+ years of hands-on Google Cloud Platform experience, with deep expertise in services used for enterprise data, analytics, AI/ML, streaming, orchestration, security, networking, observability, and production operations.
• Experience supporting multi-tenant data, analytics, or AI/ML platforms with shared services, onboarding patterns, access controls, workload isolation, and platform lifecycle management.
• Experience owning or supporting production platform services with expectations for availability, incident response, change control, observability, resiliency testing, and operational readiness.

Required Technical Expertise:

• Deep hands-on expertise with Google Cloud services such as BigQuery, Dataproc, Dataflow, Pub/Sub, Cloud Composer, Cloud Storage, IAM, VPC networking, Cloud Logging, Cloud Monitoring, and related platform services.
• Deep hands-on experience with Terraform or similar Infrastructure-as-Code practices, including reusable modules, environment provisioning, access patterns, configuration management, lifecycle changes, and controlled production deployments.
• Strong understanding of cloud security and governance practices, including IAM, least-privilege access, network controls, vulnerability remediation, audit readiness, and operational controls.
• Strong troubleshooting skills across cloud infrastructure, data platform services, access controls, networking, runtime environments, integration patterns, service dependencies, and production support scenarios.
• Experience with production readiness, release coordination, change management, incident management, root-cause analysis, and operational risk mitigation.
• Ability to design, document, and implement secure, scalable, and supportable platform patterns for enterprise cloud environments.

Preferred:

• Experience with Dataiku, Vertex AI, MLOps platforms, predictive analytics platforms, feature engineering
• workflows, model execution environments, or AI/ML workload hosting.
• Experience with streaming, event-driven, or real-time processing patterns using technologies such as Pub/Sub, Dataflow, Kafka, or equivalent services.
• Experience supporting multi-tenant cloud platforms, internal developer platforms, shared engineering services, or platform-as-a-service models in large enterprise environments.
• Experience supporting disaster recovery exercises, resiliency validation, recovery playbooks, operational readiness reviews, and continuous improvement of recovery procedures.
• Experience with SRE practices, observability dashboards, alert tuning, incident management, capacity planning, reliability metrics, and operational health reporting.
• Experience with CI/CD, GitHub Actions, Jenkins, automated testing, release automation, policy-as-code, or platform lifecycle automation.
• Experience with cloud cost optimization, capacity planning, workload placement, and performance tuning for enterprise data or analytics platforms.
• Experience working in financial services or another highly regulated enterprise environment.
• Experience partnering with business-aligned technology teams, data science teams, analytics teams, or application teams to enable governed platform capabilities.  

In addition to the salary range, this role is also eligible for bonus or incentive opportunities