Analytics Engineer with 8+ years experience, including building production data pipelines, analytics infrastructure, and automation systems. Currently at Instacart (Toronto), working on enterprise platform integration and deployment. Previously at StackAdapt, where I architected end-to-end data solutions from ingestion to insights.
What I do:
- 🔧 Build data pipelines that process millions of rows daily
- 📊 Transform raw data into actionable insights using dbt + Snowflake
- 🤖 Automate workflows that eliminate manual processes
- 📈 Create self-service analytics tools for cross-functional teams
Solution Delivery Analyst @ Instacart
- Deploying and configuring enterprise white-label e-commerce platforms across GCP, Firebase, and AWS
- Implementing SSO identity provider integrations and validating end-to-end auth flows
- Managing cross-system deployment coordination across Terraform-provisioned infrastructure
Data Architecture Analyst @ StackAdapt
- Designed and operated production ELT pipelines on Snowflake, integrating APIs, databases, and event streams
- Architected end-to-end operational analytics platforms (API → Snowflake → Tableau/ThoughtSpot)
- Built advanced analytical data models including user/device journeys, audience overlap, and reach/frequency calculations
Analyst, Programmatic Media @ StackAdapt
- Built automated reporting pipelines and analytical data models for internal and external stakeholders
- Developed a Python-based GUI tool enabling self-service data extraction for non-technical users
- Designed standardized dashboard frameworks in Tableau and ThoughtSpot
Built an event-driven analytics pipeline integrating Salesforce REST API, Snowflake, and Kestra for automated opportunity tracking. Processed real-time sales data to generate insights on pipeline health, rep performance, and revenue forecasts. Automated delivery of AI-generated reports and visualizations through Slack and Asana API.
Kestra Python Salesforce Snowflake AI Analytics Slack Asana REST API
End-to-end attribution pipeline analyzing how different models (first-touch, last-touch, linear, time-decay, U-shaped) value marketing channels. Processed 200+ conversions from GA4 + synthetic ad data.
Insight: Last-click attribution significantly undervalues prospecting campaigns
Snowflake dbt Tableau GA4 Marketing Analytics
Built end-to-end fraud detection pipeline using Snowflake, dbt, and Python. Loaded 1.3M transactions, engineered 15 features, and automated predictions with Snowflake Cortex ML.
Key Finding: Simpler models (6 features) outperformed complex ones (18 features)
Snowflake dbt Python SQL ML Operations
primary_stack = {
"Data_Warehouse": ["Snowflake", "BigQuery", "Redshift"],
"Transformation": ["dbt", "SQL", "Python"],
"Orchestration": ["Kestra", "Nomad", "Airflow-concepts"],
"Visualization": ["Tableau", "ThoughtSpot", "Power BI"],
"Languages": ["SQL", "Python", "R"],
"AI_Integration": ["Gemini", "Snowflake Cortex", "LLM Workflows"]
}
