Suresh V.

Azure Data Engineer @ Intuit

About

I didn't get into data engineering thinking about scale or architecture.It started with small things - cleaning messy datasets, writing scripts that broke more often than they worked, and trying to understand why the same query behaved differently on different days.Over time, those small problems grew into bigger ones - pipelines that needed to handle millions of records, systems that couldn't afford to fail, and data that multiple teams depended on every day.That's what pulled me deeper into this field.Today, I focus on building data systems that are not just functional, but reliable under pressure. Most of my work sits at the intersection of data pipelines, performance, and system design - making sure data flows cleanly from source to insight.Outside of work, I like staying active - usually on a cricket pitch or a badminton court. It's a good balance, and surprisingly, not very different from engineering: timing, adaptability, and quick decisions matter in both.

Country

City

Canada

Industry

Information Technology & Services

Skill

Microsoft SQL Server, Large-scale Data Processing, Pandas (Software), Data Architects, Amazon Web Services (AWS), Amazon Athena, Google Cloud Platform (GCP), Microsoft Azure, Airflow, ETL/ELT, Data Modeling, Data Warehousing, Apache Spark, Apache Kafka, Python (Programming Language), SQL, Market Research, Corporate Finance, Leadership, Microsoft Office

Experience

Azure Data Engineer

Intuit

2023-11 - Present · 2 yrs 11 mos

Toronto, ON

🔹 Designed and implemented scalable ETL pipelines handling 10M+ records/day 🔹 Built data processing workflows using Apache Airflow , reducing manual intervention by 60% 🔹 Optimized Spark jobs, improving processing performance by 35% 🔹 Developed and maintained data models for analytics and reporting 🔹 Collaborated with data scientists and analysts to deliver clean, structured datasets

AWS Data Engineer

S&P Global

2022-11 - 2023-10 · 1 yr

Toronto, ON

🔹Automated AWS infrastructure provisioning using Terraform 🔹Achieved a 90% reduction in manual effort and measurably improved system uptime and reliability 🔹Integrated AWS DynamoDB with Lambda functions to process and archive streaming events 🔹Performed Hive query optimization to improve analytical query execution times 🔹Orchestrated large-scale data movement using AWS EMR, transferring and transforming data between Amazon S3 and DynamoDB

Data Engineer

Infosys

2019-12 - 2022-7 · 2 yrs 8 mos

Toronto, ON

🔹 Developed batch and real-time data pipelines using python and SQL 🔹 Integrated multiple data sources including APIs and streaming platforms 🔹 Built and maintained batch and streaming pipelines using python and Spark 🔹 Ensured data quality and reliability through validation and monitoring systems 🔹 Designed data warehouse schemas for analytical use cases

Data Engineer

Metropolis Healthcare Limited

2017-9 - 2019-11 · 2 yrs 3 mos

India

🔹Deployed Jenkins CI/CD pipelines on Kubernetes with Docker containerization 🔹Migrated legacy SAS-based metrics to Snowflake on Azure, modernizing the analytics stack 🔹Designed scalable ETL pipelines using AWS Glue, Python, and Amazon Redshift, converting high-volume healthcare data into structured analytical formats. 🔹Built Microsoft Fabric Data Factory and Synapse Analytics pipelines to process, transform, and load data into Lakehouse environments for enterprise reporting.