Sanchita Sonar

• Design and develop scalable data pipelines using PySpark in Azure Databricks, Azure Data Factory, Delta Lake, and ADLS Gen2 to support enterprise analytics and AI-driven initiatives.
• Build reusable ETL frameworks, implement CI/CD pipelines using Azure DevOps, and promote coding standards and best practices to improve deployment efficiency and data pipeline reliability.
• Collaborate with solution architects, business stakeholders, and cross-functional teams to design Lakehouse architectures, optimize Spark workloads, conduct code reviews, and ensure data quality, governance, and high-performance data solutions.

• Implemented Azure AI Document to automate extraction of
key fields from tax documents and financial statements for EisnerAmper, reducing manual data
entry by 70%. Handled semi structured and unstructured data like JSON, Parquet, PNG, JPEG.
• Leveraged Azure Cognitive Search and OpenAI Service to index, analyze, and summarize client
tax records, enabling faster audits and executive-ready insights.
• Engineered end-to-end Azure data solutions for global clients, covering architecture design, data
modeling and partitioning strategies, data ingestion, and transformation workflows for J&J.
• Implemented data governance and quality validation frameworks, ensuring data accuracy,
consistency, and compliance with enterprise standards.
• Developed and optimized ETL pipelines using Azure Data Factory (orchestration), Databricks
(analytics), Synapse Analytics (data warehousing), and Delta Lake Storage (data management).
• Accelerated data processing by 40% through performance tuning of ETL pipelines (batch
processing), improving efficiency for key clients.
• Supported large-scale cloud migration efforts, validating mount points, testing end-to-end
pipelines, and performing comprehensive data quality checks.
• Ensured high pipeline reliability by identifying and resolving critical data issues, maintaining
uninterrupted business operations.
• Collaborated with business stakeholders to capture requirements and deliver scalable,
actionable data solutions aligned with strategic objectives.
• Authored comprehensive technical documentation, including solution designs, pipeline
workflows, and standard operating procedures.
• Mentored and onboarded new team members, accelerating their proficiency in Azure-based
data engineering for major healthcare and financial clients.

• Developed predictive model to forecast parts shortages in upcoming months, leveraging feature
engineering & clustering techniques to classify risk levels across different parts categories.
• Collaborated with international teams across Asia to automate supply chain processes, reducing
cycle time and increasing operational efficiency.
• Optimized large-scale data queries to improve processing speed and reliability for critical supply
chain datasets.

• Engineered an automated data migration pipeline to seamlessly transfer data across
Development, QA, and Production environments, generating SQL commands and reducing
manual effort.
• Optimized data comparison and SQL generation processes, cutting time complexity to 25% of
the original, significantly accelerating deployment cycles.
• Troubleshot and resolved daily L1 Jira tickets, ensuring system stability and uninterrupted
platform operations for key stakeholders. Performed unit testing to optimize code deployed.