Senior Data Engineer: Cineplex Entertainment

April 2024 – Present

• Platform Migration & Modernization: Spearheaded the migration of 40+ legacy data pipelines from on-premises infrastructure to Databricks, establishing a scalable cloud-native data platform. Orchestrated end-to-end migration strategy, ensuring zero data loss and minimal business disruption while modernizing critical data workflows.
• High-Impact Performance Optimization: Re-architected 25-year-old legacy OLAP cubes into modern star schema data models (Media360, Film360) in Databricks, powering executive-level financial reporting in Power BI. Achieved 87% reduction in processing time (4.5 hours → 36 minutes), enabling real-time decision-making for C-suite stakeholders on film performance and revenue distribution across all business verticals.
• Technical Leadership & Team Management: Led cross-functional teams of 6-8 engineers on enterprise-scale data initiatives. Owned complete project lifecycle for Media360 and Film360 data platforms—strategic solutions that drive advertising pricing decisions and power the company's flagship financial reporting system, directly informing multi-million dollar revenue decisions.
• Strategic Data Product Development: Architected and deployed production-grade data products integrating multi-source film metadata (YouTube API, Wikipedia, MovieXchange). These pipelines serve as foundational datasets for advanced analytics including long-term attendance forecasting, film recommendation engines, and international market models—enabling data-driven strategic planning across the organization.
• Production Operations & Reliability Engineering: Established robust monitoring frameworks and BAU operational excellence practices, ensuring 99.9%+ pipeline reliability. Implemented proactive alerting, automated recovery mechanisms, and comprehensive data quality validations across mission-critical data flows serving enterprise reporting and ML workloads.
• Cost Optimization & Vendor Management: Identified and negotiated alternative data sourcing strategy, preventing $150K+ in annual licensing costs while maintaining comprehensive data coverage. Demonstrated business acumen in balancing technical requirements with financial constraints.
• DevOps & Platform Engineering: Championed infrastructure-as-code practices, implementing CI/CD pipelines with Azure DevOps, service principal authentication, and automated deployment workflows. Enhanced data platform security posture and accelerated development velocity through standardized engineering practices.

Data Science Intern: Kinaxis

September 2022 – December 2023

• Data Modeling & ETL: Prototyped data-driven models and optimized feature engineering processes, improving AI frameworks for supply chain forecasting.
• ML Workflow Optimization: Analyzed feature importance and implemented automation algorithms to streamline data segmentation, yielding more accurate forecasting outcomes.
• Large-Scale Data Handling: Conducted data transformations and exploratory data analysis using Python and PySpark, with a strong focus on data security, compliance, and performance.
• Agile Collaboration: Engaged in daily scrums with cross-functional teams, ensuring alignment of project deliverables with business goals.

Business Intelligence Analyst (Co-op): Co-operators

August 2021 – October 2021

• Developed BI solutions (MicroStrategy, IBM Netezza) to improve reporting and analytics.
• Optimized and validated SQL-based data extractions, ensuring accuracy and consistency.
• Analyzed relational and non-relational data structures to contribute to more efficient data pipelines.

Programming Instructor: Code Club Canada

September 2020 – September 2022

• Educated students aged 8–12 in programming basics and Raspberry Pi projects, fostering an early interest in technology.

• Certified Databricks Data Engineer Associate: Passed on March 9, 2025 (Latest Achievement)
• Machine Learning – Coursera (Offered by Stanford University), Issued September 2022

Technical Skills

• Programming: Python, SQL, T‑SQL, PySpark
• Big Data & Cloud: Databricks, Azure Data Factory, dbt, Apache Spark, Hadoop (HDFS), Azure DevOps
• Data Engineering: ETL, Data Modeling, Star/Snowflake Schema, Medallion Architecture, CI/CD, Data Orchestration
• Visualization: Power BI, Tableau, Plotly
• Workflow/Collaboration: Jira, Confluence, Git (GitHub), Agile/Scrum

Bachelor of Science, Computer Science

York University, Toronto, ON