Engineering data.
Enabling intelligence.
I’m Suranjit. I build scalable lakehouse pipelines and AI-powered products that turn complex data into confident decisions.
MY CAREER ACROSS DATA, MEDIA & INTELLIGENCE
I build the data infrastructure behind modern analytics and AI — designing scalable lakehouse pipelines, governing data platforms, and shipping AI-powered products that turn raw data into decisions.
From insurance to digital media, I architect medallion lakehouses on Azure Databricks — Lakeflow Declarative Pipelines, Delta Lake, Unity Catalog, and config-driven PySpark frameworks built to scale, govern, and last. An early adopter of agentic development on Databricks, pairing Claude Code with the Databricks MCP server to ship faster. Based in Toronto.
This Portfolio
A personal space for my work in data engineering, lakehouse architecture, and AI. Built for the web, with live GitHub activity.
Latest commits
Most recent public pushesThe tools behind the work.
dbtAt Stratis
Databricks
Python
PySpark / Spark SQL
Microsoft Azure
Azure DevOps
AWS
Google Cloud
BigQuery
Claude Code / Cowork
GitHub
Git
Jira
Confluence
Streamlit
Neo4j / Cypher
SalesforceDatabricks
- Delta Lake
- Lakeflow Pipelines (DLT)
- Unity Catalog
- Databricks Apps
- Workflows · Asset Bundles
- Auto Loader
- Genie · Lakebase
AI & Agentic Tooling
- Claude Code
- Databricks MCP Server
- Databricks CLI
- Omnigent
- Claude Cowork
Languages & Cloud
- Python · PySpark
- Spark SQL · SQL
- Azure ADLS · ADF
- Azure DevOps · Logic Apps
- AWS S3 · GCP BigQuery
Methods & Tools
- dbt · Data transformation
- Medallion Architecture
- Metadata-Driven Ingestion
- Data Governance
- CI/CD · Performance Tuning
- Streamlit · Jira · Confluence
Lakehouse architecture & pipelines
Design and build medallion lakehouses on Databricks — Lakeflow Declarative Pipelines with data-quality expectations, CDC, Auto Loader, and Unity Catalog governance, engineered for schema-resilient ingestion at scale.
AI-powered data products
Ship Databricks Apps and Genie/Lakebase products that put live data in users' hands — from natural-language querying for underwriters to LLM-powered column mapping that eliminates manual ingestion fixes.
CI/CD & platform modernization
Modernize release workflows with Databricks Asset Bundles and Azure DevOps across dev, qa, uat, and prod — improving release velocity, environment portability, and onboarding time for new sources by ~60%.
Performance & cost tuning
Cut Spark runtimes by ~40% with Predictive Optimization, liquid clustering on serverless compute, and join-strategy tuning — lower cloud spend, faster insights.
Agentic engineering enablement
Bring agentic workflows to your data team — pairing Claude Code with the Databricks MCP server and CLI to scaffold pipelines, generate tests, and debug jobs, shipping projects ahead of schedule.
Data & Intelligence Engineer
- Building the agentic AI platform for MarTech and large banks.
Current stack: dbt
Senior Data Engineer
- Built an AI-powered policy & claims app with Databricks Genie and Lakebase — natural-language querying for underwriters with claims notes persisted to a Lakebase OLTP store, cutting insight turnaround from days to seconds.
- Developed a Databricks App for bordereaux (BDX) file intake with Claude LLM-powered column mapping — auto-normalizing headers and mapping columns to the target schema, eliminating manual fixes in lakehouse ingestion.
- Built declarative medallion pipelines (bronze → silver → gold) on Lakeflow with data-quality expectations, CDC, Unity Catalog governance, and Auto Loader for schema-resilient ingestion.
- Designed config-driven PySpark ETL frameworks reused across 30+ brokers' data and modernized CI/CD with Asset Bundles and Azure DevOps — ~40% faster Spark jobs, ~60% faster source onboarding.
- Adopted agentic engineering workflows pairing Claude Code with the Databricks MCP server and CLI to scaffold pipelines, generate tests, and debug jobs — shipping multiple projects ahead of schedule.
Data Engineer
- Built Databricks and PySpark pipelines on GCP integrating BigQuery, Bigtable, and Cloud Functions — improving reporting performance by 60%.
- Delivered multi-cloud solutions across GCP, Azure, and AWS; led data governance for enterprise data warehousing.
Data Engineer
- Designed graph data models with Neo4j and Cypher; built ETL pipelines on AWS (S3, Athena, SageMaker, DataBrew) for digital marketing analytics.
Data Analyst
- Built Azure analytics services ingesting Salesforce data for media campaign reporting with near-zero downtime.
Digital Analyst
- Architected Datorama solutions for Nestlé and CPG clients, improving campaign ROI measurement.
M.Eng, Computer Engineering
University of Ottawa
B.E, Computer Science
Visvesvaraya Technological University
Databricks Certified Data Engineer Associate
Databricks
AWS Certified Cloud Practitioner
Amazon Web Services · Valid through Dec 2026
Databricks Data + AI Summit 2026
San Francisco · Hands-on training on Lakeflow, Lakebase, and Genie