Your Team Responsibilities
The Senior Associate, Client Insights and Analytics will be integral in leveraging data to conduct analysis and draw insights that empower stakeholders to make informed business decisions. The potential candidate will bring their own strong logical reasoning and analytics skills, consulting with stakeholders to ensure they are asking the best questions possible to improve business results. This role involves a high level of collaboration with Business Technology along with other stakeholders across the Marketing, Product and Sales organizations.
Your Key Responsibilities
AI & Data Enablement Responsibilities
- Build and operationalize canonical marketing datasets and data dictionaries that enable future AI and LLM tools to query standardized data.
- Prototype AI-assisted workflows that accelerate insight generation and campaign analysis.
- Validate and contextualize AI outputs for accuracy and business alignment.
Core Analytics & Reporting Responsibilities
- Collaborate with Business Technology on the integration of data from multiple sources to build a comprehensive view of marketing performance, including the development and design of data ingestions, data transformations and modelling, UAT reviews, and ensuring data quality and accuracy.
- Develop and maintain performance reports in Power BI to track marketing effectiveness inclusive of identifying appropriate business metrics and their definitions, building strong visualizations and ensuring automation and flexibility.
- Derive insights from various data sources to understand the effectiveness of initiatives and collaborate with digital journey managers to evaluate optimizations made in flight along with ideation on opportunities to improve the customer experience
- Support the market research team with data requests, segmentation, and other analytical needs.
- Support other team members with data and insights for quarterly business reviews, monthly reviews, campaign wrap reports, budget allocation, scenario planning, and other analytics based projects.
- Manage process for marketing campaigns to ensure good data hygiene and tracking (ex. Salesforce campaigns, .com tracking, platform and UTM naming/taxonomy, lead generation optimization, etc.)
- Handle various ad hoc data and analysis requests as needed.
Your skills and experience that will help you excel
- Demonstrated proficiency in working with large datasets using SQL to conduct analysis or build reporting, ideally with a focus on marketing, product, and sales datasets (e.g., Salesforce, Pardot, Google Analytics) and using modern warehouses (e.g., Snowflake, BigQuery, Power BI data flows)
- Must understand data ingestion methods and be able to collaborate with data engineers and modelers
- Experience with Power BI (DAX and visualization)
- Experience with campaign measurement and marketing metrics/tracking.
- Experience working in an analytical role within a B2B environment
- Strong analytical reasoning skills, with the ability to collate data from multiple sources into a coherent narrative with relevant business implications and specific recommendations. Experience presenting to stakeholders.
- Excellent communication and presentation skills, both in written and verbal capacities.
- Experience using AI to improve analytical workflows: prompt design, understanding of model outputs and an appreciation for data lineage / model governance.
- Strong attention to detail and accuracy in data analysis and reporting.
- A proactive self-starter with the resourcefulness to problem-solve and work asynchronously.
- Flexibility to accommodate a global working environment (2nd or 3rd shift may be required on occasion)
- Experience with media platforms (e.g. Google Ads. LinkedIn Ads, Meta, Demand base, in App marketing) and/or advanced marketing measurement (geo testing, causal inference, MMM)
- Experience in analytics engineering and building data models specifically using a CDP or telemetry data
- Experience experimenting with generative AI tools or developing Python scripts for advanced analytics (e.g., lead scoring, churn prediction, next-best-action models).
