Manager, Customer Analytics

Jobber•Toronto, ON

4d•Hybrid

About The Position

Jobber is seeking a Manager, Customer Analytics (Post-Sales) to join their Analytics & Insights team. This role focuses on leveraging data to drive business decisions and enhance the customer experience for small home service businesses. Jobber provides technology solutions for quoting, scheduling, invoicing, and payments, aiming to transform how services are delivered. The company has a strong culture recognized by various accolades and is experiencing significant growth. The Analytics team operates within the Strategy & Analytics department, serving as an internal consulting function to connect data insights with business outcomes across the organization.

Requirements

Proven experience as a people leader in analytics, with a strong track record of developing, coaching, and empowering high-performing analysts.
Deep expertise in Customer Success, post-sales, customer lifecycle, or SaaS business analytics, with hands-on experience analyzing onboarding, customer engagement, support performance, retention, churn, expansion, or account growth.
Strong business acumen and strategic judgment, with the ability to shape initiatives, influence priorities, and connect analytical work to company-level outcomes.
Expert-level SQL skills, with the ability to work efficiently across complex relational data structures and review analytical logic with confidence.
Proficiency with BI and data visualization tools, such as Tableau, with a focus on creating clear, compelling, and actionable reporting and narratives.
Demonstrated experience with foundational and advanced analytics techniques, including performance measurement, exploratory analysis, impact evaluation, scenario analysis, experimentation, simulation modelling, and predictive analytics.
Practical fluency with AI-assisted analytics workflows, with the ability to help analysts use AI responsibly, validate outputs, and own the quality of the work.
Strong stakeholder management skills, with the ability to build trust, clarify ambiguous asks, manage competing priorities, and influence leaders through data-backed recommendations.
A passion for storytelling through data, with exceptional communication skills and the ability to distill complex analyses into clear, influential insights.
A solid understanding of SaaS business models, customer health metrics, retention dynamics, expansion levers, and the operational drivers of long-term customer value.
Intellectual curiosity, creativity, and adaptability in solving open-ended business problems in a dynamic environment.
Proven ability to thrive under pressure, maintain focus amid ambiguity, and bring structure to complex or competing demands.
Be a strong people leader who creates clarity, raises the bar, and helps analysts do the best work of their careers.
Be proactive and resourceful, with a bias for action. You’re comfortable navigating ambiguity, solving conceptual problems, corralling resources, and delivering results.
Communicate with clarity and confidence. You actively listen, empathize with stakeholders, and translate complex concepts into simple, actionable insights.
Be comfortable making trade-offs visible. You know how to say yes to the highest-impact work while helping stakeholders understand what must be deprioritized.
Care deeply about quality. You value strong analytical foundations, thoughtful QA, documentation, peer review, and reproducible work.
Be excited about the future of analytics. You see AI and automation not just as productivity tools, but as opportunities to redesign how analytics teams deliver impact.
Thrive in a fast-paced and evolving environment. You adapt quickly, embrace change, and stay focused even when yesterday’s playbook no longer applies.

Nice To Haves

Experience with Python, dbt, data modelling, customer health scoring, lifecycle analytics, or productionized analytics workflows is a strong asset.

Responsibilities

Lead and develop a high-performing team of analysts focused on strategic insights and measurable business impact.
Foster a collaborative, high-accountability team environment that values curiosity, analytical rigor, business impact, and continuous improvement.
Develop talent through mentorship, feedback, and coaching.
Prioritize team work based on business impact, stakeholder needs, company priorities, and capacity.
Translate ambiguous business questions into clear analytical approaches, actionable recommendations, and better decisions.
Partner with leaders across Customer Onboarding, Support, Account Management, Lifecycle Marketing, Product Marketing, and other cross-functional teams to define success metrics, evaluate initiatives, and uncover opportunities.
Lead high-impact analyses on onboarding performance, customer engagement, support effectiveness, customer health, retention patterns, churn and downgrade risk, expansion opportunities, and long-term customer value.
Develop frameworks, models, dashboards, and decision-support tools that connect insights across the customer lifecycle.
Translate business outcomes into analytical roadmaps.
Help leaders understand not just what happened, but why it happened, what it means, and what Jobber should do next.
Establish a clear engagement model for Customer Analytics stakeholders, including prioritization, scoping, routing, and delivery of work.
Balance functional stakeholder support, cross-functional initiatives, recurring reporting, migration work, enablement, and strategic analysis.
Identify recurring or repeatable analytics work that should become automated, standardized, documented, or moved into self-serve.
Partner with Analytics Engineering, BI, Data Science, and other Analytics & Insights teams to improve data foundation, metric definitions, reporting infrastructure, and analytical workflows.
Act as a strategic thought partner and internal consultant to customer-facing and cross-functional leaders.
Anticipate business needs and proactively identify analytical opportunities that unlock growth, efficiency, retention, or customer experience improvements.
Frame complex business challenges into clear hypotheses, lead structured problem-solving, and deliver recommendations that influence decisions.
Champion experimentation, scenario modelling, impact evaluation, customer health measurement, and predictive analytics.
Ensure the team is focused on the decisions being made, not just the analyses being produced.
Promote strong analytics practices, including clear metric design, QA, documentation, peer review, reproducible workflows, and thoughtful ownership of recurring assets.
Build compelling, executive-ready narratives that connect insight to action.
Enable self-serve analytics and scalable reporting.
Help the team adopt AI and automation responsibly.
Encourage curiosity and innovation by exploring new data sources, analytical techniques, AI-assisted workflows, and storytelling approaches.