Marketing Analytics Engineer II
Indexed description
Major Responsibilities
Collaboration & Requirements Gathering:
- Partner with data engineering and business stakeholders to define analytical requirements and design robust data pipelines and models.
- Translate marketing and business needs into scalable data and reporting solutions.
- Support experimentation efforts and analyze marketing campaign impact.
- Design, develop, and optimize dashboards and reporting frameworks for marketing analytics and KPI monitoring.
- Ensure timely, accurate delivery of reports and insights to cross-functional teams and executives.
- Prepare and clean structured and unstructured data to ensure quality and consistency.
- Merge and extract data across multiple sources (e.g., sales, operations, consumer behavior) into unified repositories to enable holistic insights.
- Develop, deploy, and maintain statistical and predictive models using machine learning techniques.
- Perform exploratory and explanatory data analysis, benchmarking, and forecasting.
- Utilize cloud computing and open-source tools for scalable analytics.
- Define and refine marketing performance metrics aligned with business goals.
- Lead initiatives to automate workflows and improve data-driven decision-making processes.
- Provide user training and documentation to facilitate self-service analytics.
- Support less experienced analysts and promote best practices in data handling and visualization.
- Bachelor’s degree in Data Science, Computer Science, Statistics, Marketing Analytics, or a related field is required.
- 2+ years of experience in data analytics, business intelligence, or marketing analytics.
- Demonstrated experience in media mix modeling, multi-touch attribution, incrementality testing, and A/B testing.
- Advanced proficiency in SQL and/or Python.
- Strong skills in Power BI and Excel for dashboard creation and reporting.
- Familiarity with data modeling, ETL pipelines, and cloud-based platforms such as Databricks.
- Ability to manipulate and analyze unstructured data.
- Strong problem-solving, statistical, and modeling skills.
- Ability to communicate complex analytical findings to non-technical audiences.
- Highly organized, self-motivated, and able to manage multiple priorities.
- Strong collaboration and stakeholder engagement capabilities.
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