Agentic Success Portfolio Lead

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

The Digital & Agentic Success team is shaping how we scale customer success through data, automation, and AI-driven orchestration. We are building the systems, intelligence, and operating models needed to turn complex customer signals into proactive, high-impact digital and agentic journeys.

We are looking for an Agentic Success Portfolio Lead
—a data-forward builder and operator—to lead the systems, telemetry pipelines, and AI orchestration layer that powers our Digital Success OS. In this role, you will bridge data engineering, data science, AI workflows, and customer journey orchestration to support customers at scale with timely digital, agentic, and human interventions.

Build Digital Success OS: Create the systems, data flows, and journey orchestration needed to support uncovered accounts, both pilot and scale the pattern across broader customer segments.

Turn data into action: Convert product telemetry, lifecycle stage, health, contact engagement, sentiment, support, and commercial signals into customer plays, AI-agent triggers, CTAs, and human handoffs.

Close ownership gaps: Act as the accountable DRI across XFN partners to assess whether systems or process ownership is unclear.

Protect delivery momentum: Maintain the decision log, architecture principles, dependency map, and change-control process so aligned architecture is not repeatedly reopened without clear rationale.

You will own:

Data foundation and pipelines: Define requirements for clean, reliable account, contact, product, engagement, lifecycle, and health data. Partner with Data Engineering and Rev Ops to build durable pipelines, tables, schemas, tagging, and integrations.

Partnering on Analytics and modeling: Partner with the analytics team on exploratory analysis, segmentation, predictive modeling, and measurement design for adoption, churn, time-to-value risk, expansion readiness, engagement quality, and agent effectiveness.

AI-agent signal design: Partner with CS AI Agent Engineers, Data Science, AIQ, and Success AI to translate raw metrics and models into practical agent plays, prompts, retrieval logic, escalation rules, and feedback loops.

Journey orchestration: Operationalize Gainsight Journey Orchestrator, CTAs, shared mailbox workflows, audience rules, consent/contact governance, channel triggers, and journey analytics.

Digital-to-human handoffs: Define what context moves from digital journeys and AI agents to human teams, and what return signal is needed to continue the journey.

Measurement and experimentation: Work closely with analytics on cohort analysis, A/B tests, and operating reviews that show impact on adoption, MAU/consumption, engagement, agent invocation, escalation conversion, and prerequisite closure.

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. To support this goal, the baseline of our range is higher than that of the typical market range, but in turn we expect to hire most candidates near this baseline. Base pay within the range is ultimately determined by a candidate's skills, expertise, or experience.

This role may also be eligible for benefits, bonuses, commissions, and equity.

Please visit  for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

In The United…