I ✦ woo data into decisions
Data engineering that drives business outcomes.
I'm a Data & Analytics Engineer who turns messy data into decision-ready insight. I work with SSMS, Snowflake, Astronomer, Airflow, dbt, Power BI, and thoughtful modeling that business users actually adopt.
I treat data like a product. Contracts, lineage, and quality get the same rigor as code. I challenge assumptions and hunt for the simple design beneath complexity.
Outside of work, I explore ideas across tech and economics, and refine them until I can explain them simply.
What I'm Working On Now
Consulting - Near Real-time Data Pipelines
Implementing CDC patterns with Debezium and Kafka for near real-time analytics for my consulting project
Cooke - dbt Macros & Global Handbook
Creating reusable dbt macros for common data transformations and building global analytics handbook at Cooke
Data Engineering Fortune Cookie
Where I've Left a Mark
A clear line from inputs to outcomes. Showing the levers pulled and the measurable results they created
Inventory Aging
- Built a unified item–vendor data model across 10+ ERPs
- Introduced a decision layer to identify and flag "dead inventory"
- Partnered with sales to prioritize selling older stock first
- Created materialized marts with contract tests to ensure accuracy
Quality Inspection
- QC images centralized in Azure Blob storage
- Models exposed as image URLs and visualized them
- Ops PowerBI dashboard with drillable defects
- Increased visibility of defects across the division
Service Desk Response
- Capability mapping with a bridging mechanism across 12 teams
- Streamlined weekly Change Management process
- Automated snapshots in dbt to maintain history
- Improved workload visibility for proactive staffing
Building Warehouse Management System
- Designed end-to-end data architecture to integrate ERP, IoT, and inventory modules
- Implemented real-time stock visibility and location tracking
- Developed automated replenishment and picking logic
- Integrated reporting layer with KPIs for throughput, accuracy, and turnaround time
Canonical Models for Yield
- Unified yield data definitions across multiple plants and ERPs
- Standardized transformation logic in dbt for consistent metrics
- Automated historical snapshots to track yield trends
- Exposed models to BI tools for plant-level and enterprise-wide analysis
Procurement Cost Saving
- Consolidated purchasing data across 10+ ERPs for spend visibility
- Identified price discrepancies and vendor performance gaps
- Trigger negotiations for better contracts based on data-driven insights
- Implemented tracking to ensure sustained cost savings
The Data Lifecycle
I work end-to-end. Click bars to explore each stage.
Collection / Source Systems
ERPs, apps, IoT via Fivetran/Boomi/custom connectors/Azure Functions. CDC/events designed with data contracts from the start.
Career Journey
My professional evolution in data engineering
Senior Analytics Engineer
Cooke Inc.
Saint John, NB, Canada
Lead a team of Analytics Engineers, driving global analytics initiatives and ensuring delivery of scalable, high-quality data solutions.
Senior Data Consultant
LaunchRoom
Freelance
Build end-to-end pipelines with Snowflake, Databricks, Airflow, dbt, Fivetran, Trino (Starburst). Worked with Azure, AWS and GCP.
Analytics Engineer
Cooke Inc.
Saint John, NB, Canada
Design and development of optimized datasets, streamlining data access and enhancing the efficiency of data analysts.
Business Intelligence Analyst
Cooke Inc.
Saint John, NB, Canada
Gathered and analyzed data from diverse sources, including customer data, financial data, and sales data, to uncover valuable trends and patterns.
Data Analyst
Scion Infotech
India · Remote
Partnered with clients to gather requirements, translate them into functional specifications, and design scalable reporting solutions.
Technologies I use
Depth over logos. What I run in practice.
Ingest & Store • Model & Transform • Serve & Observe
Ingest & Store
- Optimized CDC and batch pipelines in Fivetran with scheduling, column selection, and historical sync strategies to reduce compute and storage costs.
- Layered staging and transformation schemas in Snowflake with automated freshness tests, schema contracts, and dbt integration for data quality.
- Multi-zone data storage strategy using S3/Blob for raw and curated zones, with lifecycle policies for cost-efficient retention.
- Real-time ingestion from Event Hub into Snowflake for low-latency analytics alongside scheduled batch loads.
- Operational reporting with SSRS backed by optimized Snowflake and SSMS queries, leveraging parameterized datasets for flexible filtering.
- Query federation with Trino (Starburst) for cross-platform data access, enabling unified analytics across multiple data sources.
Model & Transform
- Modular data transformation workflows in dbt with staging, intermediate, and marts layers, supported by automated tests and documentation.
- Complex SQL query development and optimization for analytics, ETL processes, and performance tuning across large datasets.
- Python scripting for data ingestion, transformation, automation, and integration with APIs and cloud services.
- Version control with Git and GitLab for collaborative development, branching strategies, and CI/CD deployment pipelines with automated testing.
- Implementation of data contracts to enforce schema consistency, prevent breaking changes, and improve downstream reliability.
- AI-powered development with Cursor for accelerated coding, intelligent code completion, and efficient data engineering workflows.
Serve & Observe
- Interactive and visually compelling dashboards in Power BI with DAX measures, row-level security, and optimized data models for performance.
- Centralized semantic layer to standardize business metrics, improve consistency, and enable self-service analytics across teams.
- API integrations to automate data refreshes, embed analytics, and connect with external applications for real-time insights.
- dbt Semantic Layer for unified metric definitions, lineage tracking, and integration with BI tools for consistent reporting.
- Workflow orchestration with Airflow and Astronomer to schedule, monitor, and manage complex data pipelines across multiple environments with enhanced observability.
What informs my approach
Knowledge distilled from data engineering literature
Designing Data-Intensive Applicationsby Martin Kleppmann
A deep dive into building reliable, scalable, maintainable data systems. It shapes how I design ingestion, storage, and serving so insights are trustworthy and timely.
Fundamentals of Data Engineeringby Joe Reis & Matt Housley
Pragmatic trade-offs across ingestion, modeling, orchestration, and consumption. Clarifies ETL vs ELT choices and how to build systems that last beyond the first quarter.
The Making of a Managerby Julie Zhuo
Tactics for setting expectations, giving feedback, and building trust. Guides how I mentor engineers and run projects without surprises.
A Fun Activity : ETL vs ELT
See what I do, like a 10 year old 😉 → cleaned data through stages. ETL = Transform before Load. ELT = Load before Transform.
ETL vs ELT: See the Difference
InteractiveETL
Traditional Approach
Transform data before loading into the warehouse. Clean and structure data first, then store.
ELT
Modern Approach
Load raw data first, then transform in the warehouse. Leverage warehouse compute power.
Key Difference: ETL transforms data before storage (good for structured, known schemas). ELT loads raw data first, then transforms in-warehouse (better for flexibility and modern cloud warehouses).
My Data Philosophy
Principles that guide how I approach data engineering
Outcomes Over Outputs
Data as Product
Simplicity Through Layers
Impact by the Numbers
Measurable outcomes from data engineering initiatives