Kushal

Data Engineer with 4+ years building cloud-scale ELT pipelines, dimensional data models, and Power BI reporting on Azure. Processes 1.8TB of daily data across 120M+ records, partnering with finance, sales, and operations stakeholders to translate business requirements into governed, decision-ready datasets. Delivers measurable outcomes: 42% faster processing, 31% lower compute cost, 48% fewer production data issues.

• CI/CD
• Git
• GitHub
• Jira
• MySQL
• PostgreSQL
• Power BI
• Python
• SQL
• Tableau

1. Data Engineer

   LTIMindtree

   sept. 2024 to Aujourd’hui

   Ontario, Canada

   • Built ELT pipelines in Python, PySpark, Azure Databricks, Delta Lake, and Airflow processing 1.8TB daily from APIs, cloud storage, and databases, cutting batch runtime 42%, lowering compute cost 31%, and raising reliability to 99.8%. • Authored business requirements documents, functional specifications, and source-to-target mappings for 20+ pipelines, running gap analysis against legacy reports and driving UAT sign-off with business owners. • Engineered features and deployed statistical anomaly detection using z-score and seasonal decomposition baselines, tracking experiments in MLflow and cutting production data issues 48%. • Modeled Star Schema data marts and SCD Type 2 dimensions across 120M+ records, giving finance, sales, and operations governed self-serve Power BI reporting. • Designed Unity Catalog governance with role-based access controls, lineage tracking, and metadata management across enterprise Lakehouse environments. • Automated CI/CD releases with Azure DevOps and Databricks Asset Bundles (DAB) across development, test, and production.

2. Data Engineer

   Mphasis

   mai 2021 to août 2023

   India

   • Elicited requirements from finance, operations, sales, and customer stakeholders, documenting process maps, user stories, and data dictionaries in Jira and Confluence for 15+ reporting use cases. • Built ETL pipelines with Python, SQL, Azure Data Factory, and Databricks consolidating multiple source systems into a centralized analytics platform. • Modernized legacy workflows through Databricks migration and orchestration automation, cutting processing time 45%. • Optimized SQL using indexing, CTEs, window functions, partitioning, and schema redesign, cutting report generation time 52%. • Analyzed reconciliation variances, built monitoring dashboards, and delivered reusable ingestion frameworks, cutting incident resolution time 40% and dataset onboarding effort 38%.

1. Sheridan College

   Diploma, Computer Engineering Technician

   Ontario, Canada

   2025

• Databricks Certified Data Engineer Associate

  Databricks