Hello! I'm
RATHIN
SHARMA
About Me
I'm a Data & Analytics Engineer who turns messy data into decision-ready insight. I work with Snowflake, 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 work, I write and teach at Archstack — a small brand where I break down the ideas behind modern data platforms: warehouses, transformation, semantic layers, and the AI/ML stack growing on top of them.
WHAT
I DO
DATA ENGINEERING
Building Scalable Pipelines
ANALYTICS ENGINEERING
Modeling for Business Impact
AI ENGINEERING
Agents, RAG & LLMs on Warehouse Data
Reduction in Dead Inventory
Faster Inspection Cycles
Faster Order Fulfillment
Improved Yield Accuracy
End-to-End
The Data Lifecycle
Click a layer to explore.
Collection / Sources
ERPs, apps, IoT via Fivetran, Dagster, custom connectors, and Azure Functions. CDC and event streams designed with data contracts from the start.
My career &
experience
Founder & Creator
Archstack
Independent
Creator brand covering modern data platforms, warehousing, dbt, Snowflake, and the AI stack growing on top — on YouTube and in writing. Tools, teardowns, and architecture breakdowns.
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.
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.
My Work
AI Engineering
Claude-Backed Internal Agent
Internal chat and agent surface that answers data questions over warehouse context. Semantic layer as retrieval surface, Claude tool use to hit live dbt models, prompt caching to keep per-request cost flat.
Technologies
Depth over logos
What I run in practice.
Ingest & Store
Model & Transform
Orchestrate & Serve
AI & Agents
AI-Native
How I build now
The two tools that changed how I ship data work.
Cursor
Daily driver
Every dbt model, pipeline, and PR runs through Cursor. I lean on Composer for multi-file refactors, Agent mode for bounded migrations, and project-level rules to keep style consistent across a warehouse of models.
Claude
Default model
Claude is what I reach for when the work is ambiguous — architecture reviews, deep SQL debugging, cross-system design. I build Claude-backed internal tools and agents on the Anthropic SDK with tool use and prompt caching.
Interactive
ETL vs ELT
See the difference in how data flows through stages.
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).
Principles
My Data Philosophy
Outcomes Over Outputs
I measure success by business impact, not lines of code. Every pipeline, model, and dashboard should answer a real question.
Data as Product
Treat data assets like products: documented, tested, versioned, and user-focused. Quality and reliability are foundational.
Simplicity Through Layers
Complex problems decompose into simple layers. Staging to intermediate to marts. Each layer has a clear purpose and contract.
Reading
What informs my approach
Designing Data-Intensive Applications
by Martin Kleppmann
A deep dive into building reliable, scalable, maintainable data systems. Shapes how I design ingestion, storage, and serving.
Fundamentals of Data Engineering
by Joe Reis & Matt Housley
Pragmatic trade-offs across ingestion, modeling, orchestration, and consumption. Clarifies ETL vs ELT choices.
The Making of a Manager
by Julie Zhuo
Tactics for setting expectations, giving feedback, and building trust. Guides how I mentor engineers and run projects.
Contact
Designed and Developed by Rathin Sharma
© 2026