• Establish and own a layered automated testing and validation framework across the data platform. • Ensure platform reliability, correctness, and scalability through stronger development-time quality controls. • Create the testing and validation foundation needed to reduce manual QA burden, improve release confidence, and mitigate business risk associated with public-facing and AI-enabled data consumption. • Focus on establishing a reliability baseline across core data products and public-facing integrations. • Provide ongoing shared ownership of the data platform and its continuous improvement. • Design, implement, and maintain an automated and extensible testing framework for the data platform, including unit testing for data transformations, integration testing across datasets, models, and platform components, regression testing for output consistency, schema and data contract validation, invariant testing for core business rules, end-to-end pipeline testing, performance and scalability testing, AI prompt regression testing, AI evaluation testing, and telemetry validation for AI and data systems. • Establish development-time safeguards to validate business logic and expected behavior prior to release. • Contribute to quality standards, release criteria, and reliability practices for core data products and public-facing integrations. • Partner with data engineering, analytics engineering, product, and AI/application teams to embed testing into development workflows. • Identify structural quality gaps and recommend scalable solutions. • Contribute to platform resilience planning, including disaster recovery and failover readiness. • Document testing standards, practices, and reusable frameworks for cross-team adoption.

• Own and operate our GCP environment (networking, IAM, compute, storage, managed services) • Design and maintain infrastructure as code (e.g. Terraform) • Ensure environments are secure, well-structured, and cost-aware • Act as a technical expert on cloud architecture decisions • Own and evolve the data technology stack (data stores, pipelines, access patterns) • Ensure data systems are reliable, observable, and fit for analytics and product use • Define standards for data integrity, retention, access, and backup • Own CI/CD pipelines and release safety practices • Design and maintain observability (metrics, logs, tracing, alerts) • Coordinate incident response, post-incident reviews, and remediation • Ensure teams can safely deploy and operate what they build • Own infrastructure-level security posture (IAM, secrets, network controls) • Coordinate and support audits, penetration tests, and risk reviews • Design and run backup and disaster recovery testing (regular, proven recovery) • Proactively identify risks and lead mitigation planning • Produce clear documentation for architecture, runbooks, and operational processes • Partner with engineers to embed reliability and security into everyday delivery • Act as an escalation point for complex operational and infrastructure issues

• Operate and evolve the data platform infrastructure, ensuring scalability, security and high availability • Automate resource provisioning with Terraform, maintaining quality standards • Support the operation of the internal AI platform, ensuring necessary resources for applications such as LLM gateways, agents and interfaces • Implement and monitor observability standards: ensure effective logging, alerts and metrics across all supported platforms • Collaborate with Data, AI and Infrastructure technical teams, providing practical solutions for critical and high-availability environments • Document technical decisions, processes, procedures and other operational best practices to facilitate team growth and stack evolution

• Learn Five9 products and processes • Gain a working understanding of contact center terminology, processes, and telephony requirements • Participate in opportunities to apply related knowledge in both practice and production environments • As knowledge develops, become more involved in actual client project implementation work • Effectively communicate with internal and external stakeholders.