Vishnu Sai

About Vishnu Sai

I am a Data Engineer with over 3 years of experience transforming complex datasets into meaningful insights across healthcare, finance, consulting, and energy domains. My expertise spans ETL pipeline development, data validation, automation, and business intelligence reporting, using tools like SQL, Python, Power BI, Tableau, Azure Data Factory, Synapse, and Databricks. At McKesson, I engineered ETL workflows that processed over 10 million healthcare records, automated deployments with Ansible and PowerShell, and supported 12+ clinical dashboards used for compliance and executive decision-making. At KPMG, I built Power BI dashboards monitoring 25+ KPIs, streamlined financial reconciliations for 600K+ transactions, and delivered data-driven insights that improved business efficiency. What sets me apart is my ability to bridge technical depth with business impact. I don’t just build dashboards or pipelines—I focus on how they empower teams to make faster, smarter decisions. I thrive in Agile environments, enjoy collaborating with cross-functional teams, and am passionate about learning emerging cloud and analytics technologies. I’m driven by curiosity, problem-solving, and the belief that data tells a story worth uncovering. Whether it’s optimizing processes, improving data quality, or designing scalable analytics solutions, I aim to deliver measurable value that aligns with business goals. Specialties: Data Analysis | ETL Pipelines | SQL | Python | Power BI | Tableau | Azure Data Factory | Synapse | Databricks | Automation

Experience

1. Data Engineer

   McKesson

   Jul 2024 — Present · US

   Designed, developed, and deployed end-to-end ETL pipelines in Azure Data Factory (ADF) to integrate structured and semi-structured healthcare data from on-prem SQL servers, REST APIs, and flat files into Azure Data Lake Storage (ADLS), ensuring data integrity, scalability, and cost efficiency across enterprise reporting solutions.• Created and optimized Spark notebooks in Databricks for advanced data transformations, including cleansing, deduplication, surrogate key generation, schema harmonization, and business rule enforcement, which improved overall data accuracy and reliability by 35%.• Architected multi-layer data pipelines following the Medallion Architecture (Bronze → Silver → Gold) to provide standardized raw ingestion, curated clean data, and business-ready dimensional models for downstream consumption by analytics teams.• Implemented Change Data Capture (CDC) strategies and Delta Lake MERGE operations to process incremental and late-arriving data, reducing pipeline runtime and storage redundancy while ensuring compliance with regulatory data retention standards.• Optimized Spark jobs in Databricks by tuning partitions, caching strategies, and shuffle operations, reducing job execution time by 30% and improving overall cluster utilization.• Applied advanced performance tuning techniques such as Delta table partitioning, Z-Ordering, schema evolution handling, and caching strategies, which enhanced query performance and reduced daily batch runtime by more than 40%.• Designed, published, and maintained Power BI dashboards with row-level security (RLS) to deliver interactive supply-chain KPIs, shipment analytics, and financial trends, enabling stakeholders to perform self-service reporting and ad-hoc analysis.• Collaborated closely with product owners, data architects, and business analysts to translate business requirements into scalable data engineering solutions, ensuring 100% SLA adherence and alignment with data governance and compliance policies.

Education

• University of the Cumberlands

  Master of Science - MS

  2023 — 2024

• KL University

  Bachelor of Technology in Electronic

  2017 — 2021