Marketing Analyst

Key Responsibilities

• Analyze customer behavior, sales, marketing, and operational data to identify trends, risks, and opportunities that support strategic growth initiatives.
• Develop a comprehensive understanding of customer acquisition, retention, profitability, and lifecycle performance to inform business strategies.
• Aggregate, clean, transform, and prepare large datasets from diverse sources, including databases, APIs, CRM systems, and internal applications, to facilitate reporting and analysis.
• Design, develop, and maintain dashboards, reports, and visualizations that effectively communicate complex insights to stakeholders.
• Conduct ad hoc analyses, customer segmentation studies, campaign evaluations, and A/B testing to support ongoing marketing and sales initiatives.
• Apply advanced analytical techniques to evaluate marketing effectiveness, sales performance, and customer engagement metrics.
• Support forecasting, planning, and performance measurement efforts by identifying key trends and providing actionable insights.
• Validate data inputs, outputs, calculations, and assumptions to ensure accuracy and reliability of analytical outputs.
• Document methodologies, data definitions, and business rules to promote transparency and facilitate future analysis.
• Communicate findings, insights, and recommendations clearly and effectively to cross-functional teams and leadership.
• Collaborate with various departments to understand business needs and develop tailored data-driven solutions.
• Continuously seek opportunities to enhance analytical capabilities, streamline processes, and expand technical skills.

Qualifications

• Bachelor’s degree in Data Science, Data Analytics, Engineering, Economics, Statistics, Marketing, Business Analytics, Finance, Mathematics, or a related quantitative field.
• Prior internship, co-op, academic projects, or professional experience involving data analysis or business analytics is highly preferred.
• Proficiency in Microsoft Excel.
• Exposure to analytical tools such as SQL, Python, R, Power BI, Tableau, or similar platforms.
• Foundational understanding of data validation, data transformation, and structured problem-solving concepts.
• Curiosity about customer and business analytics.
• Strong organizational and time management skills.
• Ability to communicate insights effectively.
• Exposure to analytical techniques like regression analysis, forecasting, A/B testing, and hypothesis testing is advantageous.