AMIT PAWAR

• Designed and developed a scalable Azure-based analytics platform using medallion architecture. • Improved processing speed by 40% and reduced latency by 25% using PySpark and Databricks. • Built standardized ETL/ELT pipelines with Azure Data Factory, Data Lake, Databricks, and Synapse. • Utilized Snowflake for data modeling, large-scale processing, and performance tuning. • Developed reusable frameworks to reduce development time and ensure on-time delivery. • Translated business requirements into optimized data models and queries using SQL and PySpark. • Orchestrated end-to-end workflows and gained exposure to Apache Airflow and Kafka. • Built Lakehouse solutions in Microsoft Fabric and integrated with Power BI for actionable business insights.

• Collaborated with Data Scientists, Business Analysts, and stakeholders to deliver data engineering solutions. • Ingested data from CSV, JSON, APIs, and databases; performed transformation and cleansing using medallion architecture across Data Lake and Data Warehouse. • Built and orchestrated ETL pipelines with scheduling, logging, and monitoring using Airflow; familiar with Kafka. • Leveraged Snowflake for complex queries, data modeling, and multi-source integration. • Optimized performance via indexing, partitioning, and materialized views; ensured scalability through cloud autoscaling and sharding. • Implemented RBAC and encryption for data security and maintained comprehensive ETL/ELT and architecture documentation.

• Worked on an HR analytics project to analyze workforce trends, evaluate HR department performance, identify process gaps, and suggest strategic improvements. • Extracted and analyzed employee data (department, experience, salary, performance) using SQL; identified turnover patterns across specific departments and low-tenure groups. • Developed a structured report card and dashboard using QlikView, enabling better workforce planning and data-driven HR decisions.

• Focused on transforming complex datasets into clear, actionable insights that drive strategic business decisions. • Conducted statistical analyses on large datasets to assess data quality and integrity while collaborating closely with developers to build scalable business solutions. • Collected, cleaned, and analyzed data from multiple sources such as Oracle DB and Qlik Sense to identify key trends and correlations. • Played a vital role in requirement gathering, solution design, testing, and validation ensuring result-driven outcomes.

Databricks Certification: Generative AI Fundamentals

Databricks Certification

Databricks Certification: Azure Platform Architect & GCP Platform Architect

Databricks Certification