At Cotality, we are driven by a single mission-to make the property industry faster, smarter, and more people-centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.
Cotality is committed to cultivating a diverse and inclusive work culture that inspires innovation and bold thinking; it's a place where you can collaborate, feel valued, develop skills and directly impact the real estate economy. We know our people are our greatest asset. At Cotality, you can be yourself, lift people up and make an impact. By putting clients first and continuously innovating, we're working together to set the pace for unlocking new possibilities that better serve the property industry.
Job Description:As a Senior Professional Technical Product Manager, you operate with a high degree of independence within an AI-native Software
Development Lifecycle, owning end-to-end SDLC readiness across complex product scope.
You lead the shaping, sequencing, validation, and delivery of work that spans systems, dependencies, and teams. In this role, you move
beyond executing within defined scope to anticipating risk, guiding decisions, and ensuring delivery remains predictable and resilient in
production.
You operate directly within the technical ecosystem (source control, CI/CD pipelines, environments, and observability), using AI to
accelerate execution while remaining accountable for decision quality, validation, and outcomes at scale.
Job Description
- AI Native SDLC Ownership & Delivery Readiness
- Leads end-to-end SDLC readiness across complex initiatives, ensuring work is clearly defined, sequenced, and execution-ready
- Uses AI-assisted workflows to accelerate planning, synthesis, and risk identification, while modeling effective prompting, validation, and output refinement within the team
- Anticipates delivery challenges and adjusts scope, sequencing, or approach to maintain forward progress
- Ensures consistent application of readiness and validation practices across workstreams
- Provides guidance to other TPMs on effective execution practices, including AI native workflows, validation discipline, and delivery readiness standards
- Technical Execution, Validation, & Release Outcomes
- Operates deeply within engineering workflows (repos, PRs, IDEs, pipelines) to understand scope, impact, and delivery risk
- Interprets technical activity to guide decisions and maintain alignment across teams
- Leads validation practices across complex work, ensuring intended behavior is confirmed before release
- Owns release readiness and go/no-go decisions across broader scope and dependencies
- Triggers deployments and promotes builds through Jenkins, ensuring controlled and coordinated release execution
- Confirms readiness signals and post-release outcomes, ensuring alignment to product intent
- Risk Management, Production Ownership & Continuous Improvement
- Anticipates and mitigates delivery and operational risks, including cross-team dependencies and downstream impact
- Leads product decision-making during incidents, coordinating with Engineering to assess impact and guide response
- Ensures incidents result in meaningful improvement by driving remediation and prevention efforts to completion
- Uses operational data and system signals to continuously improve delivery quality, reliability, and performance
Job Qualifications:Technical & Operational Acumen
- Work orchestration across teams and dependencies (Jira or equivalent)
- Source control and code review interpretation (GitHub or equivalent)
- Repository/IDE inspection for system context and changes (VS Code or equivalent)
- CI/CD workflows and deployments (Jenkins pipelines, build promotion, release coordination)
- Environments and feature management (feature flags, staged releases)
- Observability signals to assess system health, severity, and risk
AI Native Execution
- Operates confidently in AI-assisted workflows across complex problem spaces
- Structures prompts, refines outputs, and validates results effectively
- Uses AI to surface risks, gaps, and dependencies earlier in the SDLC
- Continuously improves execution through refined AI workflows
Core Capabilities
- Independent decision-making and strong ownership of outcomes
- Ability to lead through