Senior Project Manager, Streaming Intelligence & Data Platform

Das ist der Job

Background in sports media, sports data, or adjacent domains (prep sports, broadcast, sports tech).

Darum lohnt es sich

Partner with the data team on agentic AI enablement—coordinating efforts to surface data models and semantic views to the broader business through AI‑powered interfaces, including usage tracking and accuracy assurance.

Facilitate cross‑team visibility across product, engineering, data, design, and QA to ensure aligned execution mapping back to PlayOn’s unified product objectives: Engaged Communities, Resilient Services, and New Customer Markets.

Own dependency mapping, risk management, and blocker resolution for strategic AI initiatives spanning multiple engineering teams.

Working fluency with AI/ML concepts and modern data platforms: you can follow discussions about model accuracy, prompt engineering, token economics, semantic layers, data governance, and human‑in‑the‑loop workflows without needing everything translated.

Proven ability to partner closely with senior product leaders, translating strategic vision into structured delivery plans and holding cross‑functional teams accountable to commitments. Deep familiarity with agile methodologies and experience managing initiatives with multiple teams, external partners, and moving parts.

Proficiency with Jira, Confluence, and modern project management tooling. Experience establishing governance standards and optimizing workflows across teams. Exposure to modern data stacks (Snowflake, dbt, Hightouch, Kafka) and event‑driven architectures, including familiarity with medallion architecture patterns (Bronze/Silver/Gold).

Job Responsibilities Serve as the primary delivery partner for the Streaming Intelligence product line and DPT, converting roadmap priorities into actionable project plans with clear milestones, owners, and dependencies.

Co‑own quarterly and annual planning with the Principal PM—from ideation through go‑to‑market—ensuring engineering capacity, vendor timelines, and business priorities are aligned.

Track and report on product line KPIs defined by the Principal PM (accuracy benchmarks, processing latency, content coverage, downstream adoption) and surface risks or deviations early.

Manage delivery cadence for platform infrastructure initiatives including data governance rollouts, real‑time streaming capabilities, and pipeline migration efforts—tracking phased delivery across multiple months with clear metrics of success.

Lead end‑to‑end program management for AI pipeline initiatives, including the game film analysis benchmarking program that evaluates vision models (e.g., Gemini Flash, Gemini Pro) against human‑tagged ground truth across 28+ annotated fields per play.

Manage the cadence of model evaluation cycles: coordinating game imports, prompt engineering iterations, accuracy scoring, and cost analysis across multiple AI models. Drive vendor integration workstreams—tracking deliverables, SLAs, and roadmap alignment with external computer vision and AI partners alongside the Principal PM.

Build and maintain project documentation for hybrid human‑AI workflows, including escalation paths for ambiguous plays, quality review processes, and fallback handling. Develop and maintain project dashboards, status reports, and executive‑level communications that increase transparency of engineering progress to the broader business.

Provide governance and optimization for Atlassian tools (Jira, Confluence) to ensure standardized workflows, consistent estimation practices, and clear traceability from strategy to execution.

Champion operational improvements to planning cycles, stakeholder coordination, cross‑org meetings, and feedback loops—creating transparency through standardized tooling and repeatable frameworks.

Actively incorporate AI tools (e.g., Claude, automation platforms) into your own delivery workflows: automating status reports, synthesizing meeting notes, generating risk analyses, and accelerating documentation.

Contribute to PlayOn’s broader AI maturity journey by demonstrating AI‑augmented delivery practices that can be adopted across the PMO and engineering organization.

Requirements 6–8+ years of experience in project or program management within software engineering environments, with a strong grasp of both the product development lifecycle (PDLC) and software development lifecycle (SDLC).

Demonstrated experience managing technical programs involving AI/ML, data platforms, or similarly complex technical domains—you understand model evaluation, data pipeline architecture, medallion/layered data modeling concepts, and iterative development cycles.

Strong communication and influencing skills with the ability to engage engineers, ML vendors, and non‑technical stakeholders with equal effectiveness. An AI‑forward personal practice—you already use AI tools to accelerate your own work and can demonstrate how automation improves delivery outcomes.

Preferred Experience with computer vision, video processing, or media pipeline programs (encoding, segmentation, metadata extraction). Familiarity with vendor management for AI/ML services, including evaluating model performance, negotiating SLAs, and managing integration timelines.

Experience with OKRs, KPIs, or strategic planning frameworks in a product‑led organization. Familiarity with AI coding assistants, agentic workflows, or process automation tools beyond basic prompt usage. #J-18808-Ljbffr