Nupur Upadhayay

Data Engineer with 4+ years of experience designing and optimizing data warehouse and data analytics solutions across Azure and cloud platforms. Strong hands-on expertise in SQL and Python frameworks (Pandas, NumPy, PySpark) for building automated, scalable data pipelines, and integrating data from APIs, flat files, cloud storage, and third-party systems. Proven track record in data quality (DQ) validation, pipeline monitoring, incremental loading, CI/CD deployments, and production support.

Familiar with Microsoft Fabric (Lakehouse, Warehouse, OneLake, Dataflows Gen2) and modern data platform / BI migration architectures.

Data Engineer - OFI Services - Bengaluru

(2026-06)

• Designed and maintained scalable ETL/ELT data pipelines using Azure Data Factory and Python to ingest and transform data from multiple enterprise source systems, supporting data warehouse and analytics needs.
• Designed data pipelines following Medallion Architecture (Bronze, Silver, Gold) principles to organize raw, cleansed, and curated datasets for downstream analytics and reporting.
• Built automated data pipelines integrating data from APIs, flat files, cloud storage, and enterprise source systems using Azure Data Factory, Azure SQL, Azure Blob Storage, and Azure Databricks.
• Developed parameterized Azure Data Factory pipelines using Copy Activity, Lookup, Execute Pipeline, ForEach, Triggers, and dynamic parameters to automate enterprise data integration.
• Implemented incremental loading strategies to improve pipeline performance, reduce execution time, and minimize data movement.
• Managed pipeline monitoring, failure recovery, root cause analysis, and production incident resolution using Azure Data Factory monitoring and logging, ensuring SLA compliance.
• Managed Dev, Test, and Production deployments using Git-based version control and CI/CD practices.
• Integrated Azure Data Factory with Azure Databricks notebooks to perform distributed data transformations using PySpark.
• Developed and optimized complex SQL queries, stored procedures, and data quality (DQ) validation logic to improve data accuracy and reporting performance.
• Collaborated with business analysts, data engineers, and cross-functional teams to gather requirements and deliver scalable, reliable data solutions.

Data Analyst - Amazon - Hyderabad

(2022-09 - 2026-06)

• Architected and optimized high-volume SQL-based ETL pipelines across Azure Synapse Analytics and Azure SQL Database (data warehouse environment), reducing reporting turnaround time by 40% while improving pipeline reliability and data availability.
• Engineered Python and Spark-driven automated data pipelines that eliminated 50% of manual processing effort, guaranteeing 100% consistency in complex operational and SaaS product datasets.
• Designed and deployed rigorous data quality (DQ) and validation frameworks to guarantee data accuracy, freshness, and trust across global operations, improving organizational data integrity by 15%.
• Built interactive Power BI and Tableau dashboards tracking customer behavior and SaaS platform metrics, delivering self-service tooling for rapid data consumption by senior stakeholders.
• Collaborated closely with cross-functional engineering, product, and data science teams to execute high-impact initiatives and define critical KPIs to optimize delivery performance.

Master of Computer Applications - MCA - MBM Engineering College (2022-07)

Bachelor of Commerce - B.Com - University of Kota (2020-12)