Senior Product Associate-Reliability & Engineering enablement

Make your mark by improving how engineering teams build, test, and release software with confidence. Join a team that turns operational pain points into durable product capabilities that increase stability, resilience, and delivery speed. Bring your product and reliability expertise to a role with strong cross-team visibility, growth opportunities, and meaningful impact. As a Senior Product Associate within the Consumer & Community Banking , you will own the delivery and day-to-day product operations for an application and environment management capability. You will partner with product, engineering, and support teams to define roadmap outcomes, translate needs into well-formed requirements, and drive end-to-end execution. You will embed reliability practices into delivery through measurable service targets, incident learning, and automation. You will guide prioritization using operational signals, user feedback, and risk-based trade-offs. Job responsibilities Product Ownership: Partner with the Product Manager to define and deliver roadmap items for A-CUBE and lower-environment management capabilities. Operational Runbook: Lead day-to-day product operations for A-CUBE, including runbooks, playbooks, incident triage flows, and post-incident follow-up. Integration & Delivery: Manage backlog of engineering work to improve stability and resiliency across production and non-production environments; coordinate integration testing and end-to-end validations. Requirements & Execution: Write epics, requirements, and user stories; prioritize backlog with clear acceptance criteria and measurable outcomes. SRE & Reliability: Champion site reliability engineering practices, SLO-driven monitoring, alerting refinement, and automation to reduce manual toil. CI/CD & Test Automation: Advocate and coordinate continuous integration/delivery and automated testing (unit, component, contract, end-to-end, production-level checks). Observability & Telemetry: Define and track metrics (SLOs, MTTR, false positive/alert rates), own observability requirements, and collaborate on telemetry collection using tools like Grafana, Datadog, Dynatrace, Splunk. Stakeholder Management: Liaise with support teams (Goalkeepers), product stakeholders, and engineering managers to align priorities, communicate release risks, and drive adoption. Governance & Hygiene: Enforce governance standards and hygiene practices for applications and environments (access, configuration, test-data, reset processes). Data-Informed Decisions: Use user research, incident data, and monitoring signals to surface high-value product enhancements and measure impact. Required qualifications, capabilities and skills Experience: 6+ years of product management or relevant SRE/engineering program management experience with demonstrated end-to-end delivery. SRE Expertise: Hands-on understanding of site reliability engineering concepts (SLO/SLI, incident lifecycle, runbooks). CI/CD & Testing: Proven experience with CI/CD pipelines and test automation integrated into delivery workflows. Observability: Practical experience with monitoring/telemetry tools (Grafana, Datadog, Dynatrace, Splunk or equivalent). Technical Fluency: Comfortable reading logs/metrics, writing acceptance criteria for engineering work, and collaborating on technical trade-offs. Agile Practice: Deep knowledge of Agile processes and use of Jira/Confluence (or JIRA Align) for planning and tracking. Communication: Strong written and verbal communication; experience running cross-functional demos, stakeholder briefings, and post-incident reviews. Self-Starter: Autonomous, prioritizes well in fast-paced environments, and executes under ambiguity. Preferred qualifications, capabilities and skills Domain Experience: Prior experience in lower-environment management, test environments orchestration, or similar operations-heavy products. Observability Depth: Expertise in white/black-box monitoring, SLO alerting design, and telemetry pipelines. Automated Testing Breadth: Experience implementing contract, component, and production-testing strategies. AI/ML Product Exposure: Experience shipping or operating products that embed AI agents or pipelines. Enterprise Context: Demonstrated success working in large, matrixed organizations and coordinating across multiple teams.