Senior Director, Data & Analytics

Cogeco•Montreal, QC

4d•Hybrid

About The Position

The Data & Analytics Crew Lead (Senior Director) occupies a hybrid leadership position that bridges the gap between long-term technical capability and immediate business delivery. This role is uniquely responsible for both "Future-Proofing" the organization's data infrastructure and ensuring that day-to-day data products, insights, and pipelines actively drive commercial success. The Crew Lead directly connects high-level organizational missions with the execution of the data strategy. You will oversee both the technical excellence and the direct squad deployment across four core data domains: Data Science, Data Engineering, Data Architecture, and Data Analytics. You are simultaneously the architect of the data factory and the director ensuring it delivers high-value outputs to the business.

Requirements

Bachelor’s degree in a technical or quantitative field (e.g., Computer Science, Data Science, Statistics, Information Systems, or Engineering)
Minimum 10–12 years of experience in the data space, featuring a deep professional literacy across the four core domains: Data Engineering (ETL/ELT, pipeline design), Data Science (predictive modeling, ML execution), Data Architecture (cloud infrastructure, data warehousing), and Data Analytics (BI, executive reporting).
Minimum of 10 years in a people-management role, with proven experience navigating matrixed organizations—ideally managing both functional talent and direct project delivery outcomes.
Excellent ability to communicate complex data architectures and AI concepts into clear business cases, financial ROI, and strategic advantages for non-technical stakeholders.
Proven track record of successfully delivering large-scale data transformation projects on time, while simultaneously restructuring team workflows and setting technical standards.
You must be willing to make tough talent and structural decisions.
Success in this role requires prioritizing the long-term health of the company over the immediate comfort of the status quo.
The ability to move beyond being a "top practitioner."
You must find professional value in building the system that enables others, rather than being the primary expert who solves every problem personally.

Nice To Haves

Master’s degree or MBA is a strong asset.

Responsibilities

Pioneer the 24-Month Data Vision: Define the long-term technical and architectural vision for Cogeco's data ecosystem, ensuring that machine learning, data pipelines, and modeling standards anticipate future market shifts.
Architect Unified Governance: Establish and enforce enterprise-wide data governance, privacy standards, and data quality frameworks to guarantee data consistency, absolute reliability, and ethical compliance across all business units.
Design Repeatable Methodologies: Standardize the fundamental "factory" logic—such as master data management, CI/CD processing pipelines, and BI semantic layers—to eliminate redundant work and technical debt.
Chair the Data & AI Governance Committee: Serve as the permanent Chair of the cross-functional Governance Committee, steering enterprise-wide alignment on data policies, evaluating AI use cases for risk compliance, and prioritizing data infrastructure investments across the business.
Remove Infrastructure Barriers: Actively identify and dismantle systemic bottlenecks, computing constraints, and data silos that slow down your squads’ ability to deliver insights at scale.
Lead Functional Leaders: Manage and coach the individual Chapter Area Leads and Team Leads for Data Science, Data Engineering, Data Architecture, and Data Analytics, guiding them to balance operational output with functional mastery.
Workforce Blueprinting & Hiring: Design the long-term hiring strategy for the data organization. Make final decisions on talent acquisition, onboarding standards, and the use of external contractors vs. internal resource building.
Safeguard Technical Benchmarks: Enforce rigorous technical benchmarks across all business units, ensuring that a specialist meets the same high standard of craft excellence regardless of which squad they are assigned to.
Continuous Upskilling: Anticipate emerging technology trends (e.g., Generative AI/LLM orchestration, advanced MLOps, real-time streaming architectures) and build continuous learning paths to upskill the entire team.
Drive Commercial Value: Partner directly with business unit executives and product owners to translate commercial goals into a prioritized data roadmap. Ensure that data products actively move business metrics (e.g., customer acquisition, churn reduction, operational efficiency).
Cross-Functional Squad Deployment: Dynamically deploy and embed data scientists, engineers, and analysts into cross-functional business squads, aligning the right technical skills to the highest-priority business initiatives.
Oversee High-Stakes Project Delivery: Serve as the escalation point and strategic director for enterprise-level data initiatives (e.g., migrating to a modern cloud data stack, launching real-time personalization models, or implementing cross-company reporting suites).
Manage the Data Portfolio: Balance the operational trade-offs between urgent, short-term business requests (e.g., ad-hoc commercial dashboards) and long-term infrastructure health.