Role: Data Analyst
Location: Canada-Remote
This person would be supporting the team on an ad-hoc basis with everyday activities within Loyalty. We would be looking for support on standard data requests, but at times helping with creating self-serve analytics tools such as automating processes using Snowflake Streamlit, or building a dashboard. The first project on the priority list is to continue the cleaning of credit card transaction data, standardizing merchants and categories from the RBC data share.
Deliverables
Data Wrangling & Preparation
Clean, normalize, and QA complex datasets (ex: credit card transaction files)
Build reproducible data pipelines and queries
Use of LLM’s to clean and standardize credit card transaction data
Analytics & Insights
Conduct exploratory data analysis to surface trends and opportunities across member segments, tiers, earning patterns, benefit usage, etc.
Develop clear, actionable insights to support marketing and cobrand decisions.
Support ad-hoc analyses, including member behaviour, spend behaviour, and partner activity
Develop interactive, user-friendly analytical applications using Snowflake Cortex + Streamlit in Snowflake.
Nice to have: Ability to build scalable, production-ready ML workflows in Snowflake (Snowpark, ML Functions, Feature Store), including end-to-end pipeline development encompassing data preparation, feature engineering, model training, testing, deployment, evaluation, and ongoing monitoring.
Dashboarding & Reporting
Design and build dashboards (Tableau) that provide intuitive visibility into KPIs, member engagement, and program performance.
Refresh and maintain reporting assets, and improve existing dashboards as needed.
Qualifications
Strong SQL and intermediate python skills
Experience building dashboards using Tableau
Experience using LLM’s for data manipulation is a nice to have, however a drive to learn quickly is a must have
Experience building analytical applications using Snowflake Streamlit is a nice to have
Demonstrated ability to clean and work with messy datasets (credit card data, transactional records, etc.).
Ability to support model development such as Customer Lifetime Value, Member Segmentation, etc. is a nice to have
Tech requirements: Snowflake, SQL, Python, Tableau
Strong communication skills with the ability to translate analytics into business insights.
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