Lead strategic customer analytics and insight
Act as a strategic thought partner and internal consultant to stakeholders across one or more high-priority Customer Analytics domains, helping them clarify business questions, evaluate opportunities, measure performance, and make better decisions.
Collaborate closely with teams across Jobber, including Revenue Operations, Strategy, Marketing Analytics, Product & Fintech Analytics, BI & Analytics Engineering, Data Science, and relevant go-to-market or customer-facing teams.
Lead deep-dive analyses across the customer journey, including acquisition, onboarding, engagement, product adoption, monetization, retention, expansion, customer quality, and long-term customer value.
Help define, refine, and govern the KPIs that matter most for the assigned domain or domains, including performance, efficiency, customer quality, customer outcomes, and downstream business impact.
Translate complex business questions into clear analytical plans, decision frameworks, and actionable recommendations.
Evaluate the impact of strategic initiatives, operational changes, go-to-market motions, customer programs, product or lifecycle initiatives, and other business priorities.
Help leaders understand not just what happened, but why it happened, what it means, and what Jobber should do next.
Support decision-making across assigned domains
Build a strong analytical understanding of the assigned domain or domains, including how different customer segments, behaviours, channels, products, teams, or motions contribute to business performance.
Identify opportunities to improve growth, efficiency, prioritization, customer experience, customer quality, and long-term value.
Support strategic planning, forecasting, target setting, business cases, and resource allocation decisions.
Partner with cross-functional teams and business leaders to improve reporting foundations, metric definitions, funnel or journey visibility, and decision-making workflows.
Analyze the quality and long-term value of different customer groups, helping Jobber optimize for durable growth rather than short-term volume alone.
Create reusable frameworks and decision tools that help teams evaluate trade-offs across growth, efficiency, customer outcomes, and operational complexity.
Drive experimentation, impact evaluation, and advanced analytics
Support experimentation and measurement strategies for strategic initiatives, including A/B tests, pilots, campaigns, lifecycle programs, product initiatives, operational changes, and customer-facing programs.
Apply advanced analytics techniques such as impact evaluation, scenario analysis, simulation modelling, forecasting, segmentation, and predictive analytics to inform strategic decisions.
Partner with Data Science on more complex modelling opportunities, such as customer scoring, prioritization, churn or expansion propensity, automation, AI-assisted workflows, or other predictive systems.
Bring strong judgment to ambiguous measurement problems, including cases where perfect experimentation is not possible and directional decision support is still needed.
Help stakeholders understand analytical confidence, limitations, trade-offs, and recommended next actions.
Build trusted stakeholder partnerships and executive-ready communication
Build trusted relationships with senior leaders and cross-functional stakeholders by understanding their goals, shaping analytical roadmaps, and proactively identifying opportunities.
Communicate insights through clear, compelling, executive-ready narratives that connect analysis to decisions and business outcomes.
Present findings, recommendations, dashboards, and decision frameworks in business reviews, leadership forums, and cross-functional meetings.
Make trade-offs visible when stakeholder demand exceeds capacity, helping teams prioritize the work that will have the greatest business impact.
Collaborate across Analytics & Insights to ensure Customer Analytics work connects cleanly with other analytics teams and pods, BI & Analytics Engineering, and Data Science.
Advance scalable analytics and AI-enabled workflows
Identify recurring or repeatable analytics work that should become automated, standardized, documented, or moved into self-serve.
Partner with BI & Analytics Engineering to improve the data foundation, semantic layer, reporting infrastructure, and self-serve capabilities that support Customer Analytics decision-making.
Use AI-assisted analytics workflows responsibly to accelerate exploration, coding, documentation, QA, and storytelling while maintaining strong ownership of output quality.
Promote high-quality analytics practices, including clear metric definitions, reproducible workflows, thoughtful QA, documentation, peer review, and data quality stewardship.
Build scalable assets, dashboards, models, and analytical frameworks that reduce manual reporting and shift analyst time toward higher-judgment work.
Ensure insights are timely, trusted, actionable, and connected to meaningful business outcomes.
Lead as a senior individual contributor, you will:
Lead one or more important Customer Analytics domains through influence, ownership, and strong judgment.
Provide mentorship, peer review, and analytical guidance to analysts working in or adjacent to those domains.
Raise the bar for analytical quality, stakeholder communication, and business impact across the Customer Analytics team.
Contribute to team-wide best practices around prioritization, documentation, QA, AI-enabled workflows, self-serve, and strategic storytelling.
Operate as a senior individual contributor who can independently own ambiguous, high-impact work while helping others grow.
To be successful, you should have:
Expert-level SQL skills, with the ability to work efficiently across complex relational data structures and validate analytical logic with confidence.
Strong experience in B2B SaaS, marketplace, fintech, revenue, growth, product, customer, or go-to-market analytics.
A deep understanding of SaaS business models and customer journey metrics, such as funnel performance, conversion, activation, engagement, retention, expansion, monetization, customer quality, and customer value.
Proficiency with BI and data visualization tools such as Tableau, with a focus on clear, compelling, and actionable reporting and narratives.
Demonstrated experience with foundational and advanced analytics techniques, including performance measurement, exploratory analysis, impact evaluation, experimentation, forecasting, scenario analysis, simulation modelling, and predictive analytics.
Strong business acumen and strategic judgment, with the ability to connect analytical work to company-level growth, efficiency, and customer outcomes.
Excellent stakeholder management skills, with the ability to build trust, clarify ambiguous asks, influence priorities, and guide leaders toward better decisions.
A passion for storytelling through data, with the ability to distill complex analyses into simple, influential insights.
Practical fluency with AI-assisted analytics workflows, with the judgment to use AI responsibly, validate outputs, and own the quality of the final work.
Intellectual curiosity, creativity, and adaptability in solving open-ended business problems in a fast-moving environment.
Demonstrated ability to mentor analysts, lead through influence, and operate as a senior individual contributor owning one or more business domains.
Proven ability to thrive under pressure, navigate ambiguity, and bring structure to complex or competing demands.
Experience with Python, dbt, Snowflake, Salesforce or other CRM/customer systems, data modelling, and productionized analytics workflows is a strong asset.
You should also:
Be proactive and resourceful, with a bias for action. You are comfortable navigating ambiguity, solving conceptual problems, corralling resources, and delivering results independently.
Communicate with clarity and confidence. You actively listen, empathize with stakeholders, and translate complex concepts into simple, actionable insights.
Care deeply about quality. You value strong analytical foundations, thoughtful QA, documentation, peer review, and reproducible work.
Be comfortable making trade-offs visible. You know how to focus on the highest-impact work while helping stakeholders understand what must be deprioritized.
Be excited about the future of analytics. You see AI, automation, and self-serve not just as productivity tools, but as opportunities to redesign how analytics teams create impact.
Lead without authority. You can influence senior stakeholders, guide peers, mentor analysts, and move important work forward without relying on formal people management authority.
Thrive in a fast-paced and evolving environment. You adapt quickly, embrace change, and stay focused even when yesterday’s playbook no longer applies.
