This role is eligible for our hybrid work model: 2 days in-office

This job posting is for an existing, currently vacant position.

Staff Portfolio Release Manager

Our Technology team is the backbone of our company: constantly creating, testing, learning and iterating to better meet the needs of our customers. If you thrive in a fast-paced, ideas-led environment, you're in the right place.

Why this job's a big deal:

As a Staff Portfolio Release Manager supporting our Data and AI/ML Platforms within a Product Operating Model (POM), your role centers on orchestrating the successful, predictable, and scalable delivery of platform capabilities that power analytics, experimentation, machine learning, and intelligent customer experiences across Priceline.

You will operate at the Platform Product Group level, driving alignment and release coordination across multiple platform teams responsible for data ingestion, self-serve analytics, experimentation frameworks, AI/ML model platforms, business intelligence, and data products. You will ensure releases are delivered with quality, transparency, and operational readiness while continuously evolving release processes to support agility, compliance, and scale.

As a staff-level leader, you will serve as a strategic partner to Engineering, Product, Data Science, and Platform leaders, reducing delivery risk and enabling teams to ship platform capabilities with confidence.

In this role you will get to:

Portfolio Release Planning & Coordination

• Own and maintain the portfolio release calendar for Data and AI/ML Platforms, coordinating releases across product, engineering, data science, platform, QA, and operations teams.
• Orchestrate complex, cross-functional releases involving data pipelines, self-service data platforms, experimentation frameworks, business intelligence capabilities, AI/ML model platforms, and platform infrastructure.
• Ensure releases are aligned to strategic business initiatives, platform roadmaps, seasonal readiness, and enterprise milestones.
• Drive release readiness reviews, confirming that testing, model validation, documentation, operational runbooks, approvals, and risk assessments are complete.
• Manage cross-team dependencies and sequencing across multiple portfolios and platform domains.

Risk, Governance & Quality Management

• Identify and proactively manage risks to release quality, timing, platform stability, and rollback readiness.
• Partner with Engineering, Data Science, SRE, and DevOps teams to establish and enforce release criteria, validation gates, model governance processes, and post-release monitoring.
• Maintain controls required for compliance, audit, or change management (especially in regulated environments).

Cross-Team Communication & Stakeholder Alignment

• Serve as the central point of coordination for Data and AI/ML Platform releases, connecting Product, Engineering, Data Science, Analytics, Security, Legal, and Operations stakeholders.
• Communicate release scope, timelines, dependencies, risks, and critical incidents to leadership and partner teams.
• Partner with Product Managers and Engineering leaders to develop release communications, change advisories, and rollout strategies.
• Drive alignment across portfolio teams operating within the Product Operating Model (POM), ensuring transparency and accountability.

Process Improvement & Automation

• Continuously improve release management processes, tooling, and reporting to support faster, safer, and more predictable platform releases.
• Partner with DevOps, SRE, and Platform Engineering teams to advance CI/CD pipelines, release automation, feature flagging, and model deployment processes.
• Support the organization's transition toward continuous delivery and modern platform engineering practices for data and AI systems.
• Develop scalable release frameworks and governance models that can be adopted across multiple portfolios and product groups.

Metrics & Post-Release Analysis

• Define, track, and report on release health metrics, including:
• Deployment frequency
• Failed deployments
• Change lead time
• Change failure rate
• Mean Time to Recovery (MTTR)
• Data quality and platform reliability metrics
• AI/ML model deployment and performance indicators
• Facilitate release retrospectives and drive continuous improvement initiatives across teams.
• Assess the operational, business, and customer impact of major platform releases.

Who you are:

• 10+ years of experience in Agile delivery, Technical Program Management, Release Management, or Portfolio Management, with significant experience supporting large-scale data and platform organizations.
• Deep understanding of how release management integrates into Product Operating Models (POMs) and modern iterative delivery frameworks.
• Experience leading complex, cross-functional release planning across Data Platforms, Business Intelligence platforms, AI/ML platforms, experimentation frameworks, or analytics ecosystems.
• Fluent in Agile methodologies and frameworks, including Scrum, Kanban, SAFe, and continuous delivery practices.
• Proven ability to coordinate dependencies across multiple teams and portfolios while balancing delivery speed with platform stability and operational excellence.
• Exceptional stakeholder management and communication skills, with the ability to influence leaders across Product, Engineering, Data Science, and Operations.
• A proactive problem solver who identifies risks and opportunities early and makes sound decisions with incomplete information.
• A detail-oriented operator who thrives in fast-paced environments and can manage multiple competing priorities.
• Acts as the bridge between rapid platform innovation and operational reliability, enabling teams to deliver data and AI capabilities with confidence.

Nice to have

• Experience supporting self-serve data platforms, business intelligence platforms, experimentation systems, or AI model platforms.
• Familiarity with modern data and AI technologies such as Snowflake, Databricks, Kafka, Airflow, dbt, MLflow, Kubernetes, and cloud-based AI services.
• Experience with AI governance, model monitoring, MLOps, and Responsible AI practices.
• Proficiency leveraging AI and automation tools to improve reporting, release processes, and operational workflows.
• Familiarity with Agentic AI technologies and AI-assisted software delivery practices.
• Understanding of data pipelines, platform dependencies, AI/ML lifecycle management, model deployment processes, and cloud-native architectures.

• Illustrated history of living the values necessary to Priceline: Customer, Innovation, Team, Accountability, and Trust.
• The Right Results, the Right Way is not just a motto at Priceline; it's a way of life. Unquestionable integrity and ethics are essential.

There are a variety of factors that go into determining a salary range, including but not limited to external market benchmark data, geographic location, and years of experience sought/required. In addition to a competitive base salary, certain roles may be eligible for an annual bonus and/or equity grant.

The salary range for this position is $135,000- $150,000K CAD.

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