Product Manager

Equinix • Toronto, ON

41d • CA$99,000 - CA$149,000 • Onsite

About The Position

We are seeking a highly technical, execution-focused, and hands-on Data Product Manager to manage the daily lifecycle of our core data assets. In this role, you will treat data as a first-class product, executing the delivery roadmap from initial data ingestion and modeling through to engineering and deployment. You will bridge the gap between complex data infrastructure and operational needs, ensuring our analytical engines, models, and pipelines deliver real-time, automated value directly to the business.
This role requires strong technical execution and product management discipline. You will not focus on front-end user experiences or static dashboards. Instead, you will engineer and optimize the underlying data structures, APIs, and algorithmic logic that power automated decision-making and fuel enterprise workflows within a modern Google Cloud Platform (GCP) ecosystem.

Requirements

• 5+ years of experience working directly with data-centric products, data platforms, data APIs, or analytics infrastructure (e.g., as a Data Product Manager, Technical Product Manager, Data Analyst, or Data Engineer)
• Experience managing a product backlog, writing technical user stories, participating in sprint cycles, and prioritizing engineering tasks
• Ability to translate defined business goals into structured data requirements, identifying the inputs and logic parameters needed
• Expert-level SQL skills to write highly optimized, complex analytical queries. Proficiency with window functions, Common Table Expressions (CTEs), nested fields, and performance tuning for massive datasets
• Hands-on experience navigating the BigQuery architecture. Competency using BigQuery features like partitioned/clustered tables, materialized views, and BigQuery ML for rapid model deployment
• Solid understanding of core GCP data infrastructure tools (e.g., Cloud Storage, Dataflow, Dataproc, Pub/Sub, Vertex AI) used to orchestrate, store, and stream data across enterprise applications
• Fundamental proficiency in Python or equivalent scripting languages to manipulate data, connect to cloud APIs, and test functional logic prototypes
• Practical understanding of basic ETL/ELT pipelines, data transformations (e.g., using dbt within BigQuery), and data warehousing concepts
• Good understanding of how APIs and microservices are used to stream data product outputs from cloud environments into target business systems
• Foundational understanding of predictive modeling, scoring algorithms, look-alike logic, and data segmentation frameworks
• Familiarity with recommendation logic architectures and dynamic optimization rules engines
• Ability to communicate clearly and coordinate effectively between technical engineering teams (Data Engineers, Data Scientists) and commercial business users
• Analytical mindset focused on troubleshooting data flow issues and translating technical complexities into clear project status updates
• Manages stakeholder expectations within and/or across functions
• Identifies and proactively includes correct stakeholders and communications effectively

Nice To Haves

• 5+ years experience preferred
• Bachelor's degree preferred

Responsibilities

• Own the Data Product Lifecycle: Deliver the roadmaps for assigned data products, tracking development from data sourcing and processing to production implementation and delivery
• Embed Analytics & Intelligence: Connect with business process owners to embed predictive models, rules engines, and data-driven insights directly into operational workflows and daily business applications
• Hands-on Prototyping: Actively build early-stage proof-of-concepts, data mockups, and SQL/Python-based logic prototypes to validate data availability, model logic, and workflow integration prior to full engineering scale
• Translate Data to Action: Transform complex data structures, raw algorithmic outputs, and data tables into clean, actionable, and context-aware guidance streams available at the exact moment decisions are made
• Monitor Product Performance: Track daily data health, product metrics, and pipeline reliability. Continuously monitor performance and refine models to resolve drift, ensure data quality, and maximize business impact
• Operationalize Insights: Maximize data utility and unlock ROI by shifting analytics out of passive, disconnected reporting tools and directly into live business execution
• Systemize Intelligence: Prevent a reliance on intuition or static strategies by systematically injecting proactive, data-driven recommendations into daily operations
• Ensure Decision Consistency: Enable reliable operational scale and higher win rates by standardizing data products and automated logic across diverse segments, regions, and customer footprints

Benefits

• Employee Assistance Program
• Healthcare coverage that is designed to complement the provincial healthcare system
• Life insurance
• Disability insurance
• Optional benefit plans
• Defined Contribution Pension Plan (DCPP)
• Group Retirement Savings Plan (RRSP)
• Tax-Free Savings Plan (TSFA)
• Vacation
• Personal time
• Various paid holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level