Senior Analyst, Marketing Insights & Analytics

Mackenzie Financial Corporation•Toronto, ON

2d•CA$65,500 - CA$91,000•Hybrid

About The Position

The Senior Analyst, Marketing Insights & Analytics is responsible for leading and delivering high-impact insights through data analytics, dashboard development, advanced analytics, and marketing performance measurement. This role demands a high degree of accountability, ownership, and proactive stakeholder engagement. Working closely with marketing, product, and data teams as well as external agencies, you will design analytical roadmaps and provide actionable recommendations to optimize marketing strategies. You will utilize data analytics techniques to extract insights, build customer segmentation for personalization using behavioral and transactional data, and lead the development of marketing measurement frameworks to ensure alignment with business goals. Strong communication and storytelling skills are essential to translate complex data into clear, compelling narratives that influence decision-making.

Requirements

Bachelor's or Master’s degree in Marketing, Business Analytics, Statistics, Computer Science, or a related field.
3+ years of experience in marketing analytics or business intelligence, with a strong focus on digital and performance marketing.
Exceptional analytical problem-solving skills to turn raw data into strategic insights and compelling narratives.
Proven experience developing campaign measurement plans, implementing measurement and KPI frameworks, and providing analytical leadership on marketing strategies and campaigns.
Advanced technical proficiency in SQL for data extraction and manipulation.
Expertise in building complex, interactive dashboards using Tableau, Looker, or other BI platforms to monitor performance and maximize effectiveness.
Deep understanding of the media and marketing landscape (advertising, mass/digital/social media buying, research), with the ability to determine appropriate channels based on business objectives.
Agility to manage multiple priorities and adapt quickly in a lean, fast-paced environment.
Comfort working with and integrating AI tools into analytical workflows.

Nice To Haves

Experience with Big Data tools (e.g., Spark, Hive, HBase), cloud platforms (AWS, Azure, GCP), and Agile methodologies.
Experience doing marketing analytics, including commercial analytics, marketing mix modelling, attribution modelling etc.
Understanding of marketing data and experience working with media platforms (Campaign Manager, Google Ads, Facebook Ads Manager, etc.).
Experience working with Adobe Analytics, Google Analytics, or similar web analytics tools.
Familiarity with software development tools and practices (Git, code review, etc.).

Responsibilities

Lead data analysis, extracting highly actionable insights from complex marketing datasets. Develop and deliver comprehensive performance summaries and compelling narratives to marketing stakeholders, including senior leadership, to drive strategic decision-making.
Champion the implementation and leveraging of AI tools within marketing analytics workflows to drive operational efficiencies, enhance data analysis, and accelerate the identification of actionable insights.
Architect and design a robust, scalable reporting ecosystem. Build, maintain and optimize automated, interactive marketing dashboards to continuously monitor cross-channel marketing performance.
Lead the development and evolution of measurement frameworks to track performance across paid, earned, and owned channels. Own end-to-end campaign analysis, translating findings into data-driven recommendations for optimization and growth.
Analyze behavioral and transactional trends to develop audience segmentation for personalization. Support marketing technology teams with A/B testing, optimization strategies, and broader campaign improvements.
Establish and enforce marketing data governance standards, including tagging frameworks, to ensure accurate attribution and reporting. Provide recommendations on advanced data modelling approaches (e.g., attribution modelling, marketing mix modelling) in collaboration with data science teams and business analytics stakeholders.