Senior Web Data Analyst

Senior Web Data Analyst

• Mine, and analyse granular hit/event level web, product, sales, and digital marketing data.
• Build Tableau dashboards to support product stakeholders.
• Build Tableau data sources that can allow for self-serve usage of product and web data.
• Build conversion funnels across multiple product lines to help product teams understand the complete customer journey.
• Develop customer segmentation models and activation testing plans.
• Work with data engineers to improve and maintain the customer360 and API data models by defining new feature requirements, making improvements to taxonomy, and identifying bug fixes.
• Work with cross-functional teams (BI Enterprise Data Warehouse, Salesforce MOPS, IT, Product teams) to gather and refine requirements for metrics and dashboards needed by product stakeholders.
• Develop a good understanding of the business s model, objectives, issues, and challenges by interacting and collaborating with leaders and stakeholders.

• Experience using Tableau Desktop/Creator, Adobe Analytics, Amplitude.
• Advanced SQL knowledge.
• Experience with cookie-level advertising platform data (Google, Bing, Epsilon, LinkedIn, Facebook etc.) and measuring demand generation KPIs (ROAS, CTRs, Impressions, MTA Attribution, MMM).
• Experience with cookie-level web/product data and KPIs like usage, conversion funnels, bounce rates, unique visitors, sessions, hits/events, pathing and journey optimization.
• Experience with Customer Journey Analytics, including mapping cross-channel touchpoints, analyzing end-to-end user flows, and identifying key moments that impact conversion.
• Experience with designing A/B/Multivariate/Lift testing and measurement for both digital and offline demand generation channels.
• Experience gathering and refining requirements from business stakeholders.
• Experience communicating dashboards and findings to both technical and non-technical audiences.

• Experience using Python, SciKit, SQL, Snowflake, hit-level Adobe Analytics data, product usage data, Jupyter Notebooks, Amazon SageMaker, Airflow, Github.
• Proficiency with data mining, mathematics, and statistical analysis techniques.
• Experience with predictive modelling techniques, unsupervised, supervised, reinforcement, causal inference machine learning techniques.
• Experience with the following ML steps: data prep, choosing model, feature creation and selection, training, evaluation, parameter tuning, making predictions.