As a Senior Data Engineer at GDIT, I led a transformative project from July 2021 to the present, focused on elevating our data transformation and analytics capabilities. This initiative involved leveraging Power Query within Power BI for data cleansing and massaging, along with designing and implementing efficient data pipelines using Azure Data Factory.I harnessed Python to ensure data quality and consistency, while Snowflake served as our robust data warehousing solution. I integrated Snowflake with ETL pipelines using tools like Apache NiFi and custom scripts, ensuring seamless data ingestion, transformation, and loading.Collaborating closely with business stakeholders, I automated critical processes, enabling data storytelling in Tableau and empowering teams to make data-informed decisions. I also developed Spark applications for data extraction, transformation, and aggregation, providing valuable insights into customer usage patterns.My contributions extended to building Tableau and Data Studio dashboards for marketing campaigns, implementing robust security measures within Snowflake, and optimizing data accessibility with Power BI reports. This project allowed me to showcase my expertise in data engineering and analytics, delivering tangible benefits to our organization.

During my role as a Senior Data Engineer at Verizon (August 2019 - June 2021), I led a project that brought a transformational shift in our data engineering and business intelligence capabilities.I orchestrated complex data pipelines using Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics to seamlessly Extract, Transform, and Load (ETL) data from various sources into Azure Data Storage services. This project also involved navigating large-scale database environments like Hadoop and MapReduce.My proficiency in Python and SQL Alchemy facilitated efficient data interaction and management with relational databases, while my expertise in tools such as Hadoop, Snowflake, Attunity, Tableau, and Power BI enabled the design and development of impactful Business Intelligence solutions. These solutions empowered different teams across the organization with insightful dashboards and reports.To ensure operational excellence, I implemented CI/CD workflows using Bitbucket Pipelines for automated testing and deployment of data engineering code and pipelines. I also excelled in database management with PostgreSQL and MySQL/MariaDB and was responsible for maintaining Snowflake clusters and Hadoop monitoring.Throughout the project, I developed Spark applications for data extraction and analysis and created interactive Tableau dashboards to visualize complex datasets. Additionally, I implemented data quality checks in Python to ensure data integrity.This project showcased my expertise in data engineering, BI, and data warehousing, resulting in significant advancements in Verizon's data capabilities, and I'm proud to have been part of this transformational journey.

During my role as a Data Engineer at Abbvie (September 2017 - June 2019), I led a project that transformed data engineering and business intelligence. I developed automated data pipelines, migrating servers and databases to AWS, and designed data models for Amazon Redshift. Proficient in Python and SQL, I created insightful data visualizations in Tableau and optimized Snowflake queries.I converted manual reporting to automated CI/CD pipelines, enabling seamless data ingestion into AWS S3 Data Lake. ETL jobs extracted data from various sources, and I designed SSIS/ETL frameworks. I customized Tableau dashboards and documented code for collaboration.My contributions extended to SAP-SD integration, SQL Reporting Services (SSRS), and AWS services for big data analytics and data warehousing. This project showcased my data engineering, ETL, modeling, and cloud expertise, significantly enhancing Abbvie's data capabilities.

During my time as a Data Engineer at Ditech (July 2015 - August 2017), I led a project focused on data engineering and analytics innovation. I used Python and Spark to develop models and algorithms for analysisI managed AWS infrastructure for high-availability, incorporated CI/CD with AWS Lambda, and empowered users with Tableau self-service capabilities. My role extended to data ingestion, automation, and predictive modeling.This project showcased my expertise in data engineering, analytics, and cloud architecture, contributing significantly to Ditech's data-driven innovation.