Neelima Dahagam

Technical Lead / Senior Data Engineer with 10+ years of experience designing, developing, and optimizing enterprise-scale cloud data platforms, modern ETL/ELT frameworks, and advanced analytics solutions across Healthcare, Financial Services, Retail, and Technology domains. Extensive expertise in Azure Data Factory, Azure Databricks, Delta Lake, PySpark, Apache Spark, Snowflake, SQL, Python, Apache Airflow, Kafka, Power BI, and cloud-native data engineering technologies. Proven experience delivering scalable Lakehouse architectures, real-time and batch data pipelines, metadata-driven ingestion frameworks, enterprise data warehousing solutions, and cloud migration initiatives while ensuring high availability, performance, security, and data quality.

Strong background in data modeling, performance tuning, DataOps, CI/CD automation, governance, and enterprise reporting with a collaborative approach to working alongside solution architects, business stakeholders, product owners, and cross-functional engineering teams to deliver reliable, secure, and business-driven data platforms.

Technology Lead/Senior Data Engineer at Toyota (2018-11 – Present)

Environment: Azure Data Factory (ADF), Azure Databricks, Delta Lake, PySpark, Spark SQL, Apache Airflow, Snowflake, Azure SQL Database, Power BI, Python, SQL, Azure DevOps, Git, dbt

• Designed, developed, and implemented scalable cloud-native ETL/ELT pipelines using Azure Data Factory and Azure Databricks to support enterprise-wide batch and real-time data processing.
• Architected high-performance PySpark data transformation frameworks leveraging Delta Lake, enabling incremental data ingestion, schema evolution, ACID transactions, and reliable production data processing.
• Designed conceptual, logical, and physical enterprise data models supporting scalable analytics, operational reporting, and self-service business intelligence solutions.
• Developed and optimized Star and Snowflake dimensional schemas, fact tables, and dimension models to improve reporting performance and enterprise data accessibility.
• Built enterprise-scale Lakehouse Architecture using Medallion (Bronze, Silver, Gold) design patterns, delivering governed, reusable, and high-quality datasets for downstream analytics.
• Optimized Delta Lake performance through partitioning, caching, file compaction, and query tuning, significantly improving data processing efficiency and workload performance.
• Engineered metadata-driven ETL frameworks with robust data validation, schema management, and automated error handling to improve pipeline reliability and maintainability.
• Developed interactive Power BI dashboards integrated with Azure SQL and Databricks, utilizing advanced DAX calculations and optimized semantic models to support executive decision-making.
• Designed and maintained Apache Airflow orchestration workflows with automated scheduling, dependency management, SLA monitoring, retry mechanisms, and operational alerting for production workloads.
• Implemented centralized logging, monitoring, and pipeline observability frameworks to improve operational visibility, proactive issue detection, and enterprise data governance.
• Collaborated closely with solution architects, business analysts, product owners, and cross-functional engineering teams to deliver scalable, secure, and business-driven cloud data solutions.
• Followed Agile Scrum methodologies, CI/CD best practices, version control, and code review standards to ensure high-quality, maintainable, and production-ready data engineering solutions.

Data Engineer at Infosys (2016-11 – 2018-09)

Client: Bank of America | Environment: SQL Server, SSIS, SSRS, Power BI, SQL, T-SQL, Python, Azure Data Factory, Azure SQL Database, Git

• Designed, developed, and optimized scalable ETL pipelines using SQL Server and SSIS to integrate data from multiple enterprise systems into centralized data warehouses.
• Developed robust data transformation and cleansing processes using SQL and T-SQL to improve data quality, consistency, and reporting accuracy across business functions.
• Designed and implemented Star and Snowflake dimensional data models to support enterprise analytics, business intelligence, and operational reporting.
• Built and optimized complex SQL queries, stored procedures, views, and indexing strategies to improve query performance and large-scale data processing efficiency.
• Developed automated data ingestion and validation frameworks to ensure reliable, accurate, and timely delivery of enterprise data.
• Integrated structured and semi-structured data from relational databases, flat files, and external sources, supporting scalable analytics and enterprise reporting initiatives.
• Collaborated with business analysts, data architects, and cross-functional teams to gather data requirements and deliver scalable, business-driven data engineering solutions.
• Performed data profiling, reconciliation, and production support while identifying performance bottlenecks and implementing optimization strategies for enterprise data platforms.

Data Engineer at Xoriant (2016-03 – 2016-09)

Client: KPMG | Environment: SQL Server, SSIS, T-SQL, SQL, Power BI, Excel, SSMS, Visual Studio, JSON, Windows Server

• Assisted in developing and maintaining ETL pipelines using SQL Server to extract, transform, and load data from multiple enterprise systems into centralized data repositories.
• Wrote and optimized SQL queries, stored procedures, and views to support data transformation, reporting, and daily business operations.
• Performed data cleansing, validation, and reconciliation to ensure data accuracy, consistency, and integrity across enterprise datasets.
• Supported the development of Star and Snowflake data models for business intelligence and reporting solutions.
• Assisted in integrating structured and semi-structured data from relational databases, flat files, and external data sources.
• Monitored ETL job executions, investigated pipeline failures, and resolved data quality issues to ensure reliable data delivery.
• Collaborated with senior data engineers, business analysts, and reporting teams to gather requirements and support data integration initiatives.
• Supported Power BI reporting by preparing datasets, validating data accuracy, and optimizing SQL queries for improved report performance.

BI Developer at Dynamic Computer Services (2015-09 – 2016-02)

Client: Accenture | Environment: SQL Server, SSRS, T-SQL, SSMS

• Developed complex SSRS reports, dashboards, stored procedures, and T-SQL queries supporting healthcare reporting, regulatory compliance, and operational analytics.
• Designed optimized SQL queries, indexing strategies, and database objects to improve reporting efficiency and reduce execution time for large healthcare datasets.
• Collaborated with business users and technical teams to gather reporting requirements and develop scalable reporting solutions supporting Medicaid business operations.
• Performed data validation, reconciliation, and quality assurance to ensure accuracy, integrity, and consistency of enterprise reporting data.
• Supported production deployments, report enhancements, troubleshooting, and performance tuning activities in a fast-paced healthcare environment.

Software Engineer at Emerson Process Management (2014-06 – 2015-08)

Environment: ASP.NET, C#, SQL Server, T-SQL, HTML, JavaScript

• Designed, developed, and maintained enterprise web applications using ASP.NET, C#, and SQL Server to support business-critical operational processes.
• Developed reusable