Jul 2024 – Jun 2025 · 11 mos · United States · Remote

Jul 2024 – May 2025 · 10 mos · Arizona, United States · Remote

Sep 2023 – Present · 2 yrs 9 mos · United States · Remote

Feb 2021 – Jul 2022 · 1 yr 5 mos · Pune, Maharashtra, India

• Delivered enterprise-scale data engineering, analytics, and consulting solutions for a major global client through the Data Ocean MPA project. Modernized architecture from Wherescape 3D to RED using Data Vault modeling, improving scalability and governance.
• Optimized ETL pipelines with Apache Airflow, reducing failures by 20% and improving performance by 65%.
• Designed data integration workflows for batch and streaming data ingestion, ensuring high availability and data quality.
• Managed a team of 5 engineers, providing technical leadership and consulting guidance for onboarding and process improvements.
• Deployed IBM Cloud data pipelines, cutting processing time from 10 to 7 hours and achieving a 98% data issue resolution rate.
• Led AWS Cloud migration (RDS, S3, Snowflake), reducing downtime by 20% and enhancing data processing scalability.
• Created Tableau and Power BI analytics dashboards, delivering actionable insights for executive decision-making.
• Implemented data governance, encryption protocols, and compliance practices, strengthening data security and trust.
• Coordinated with clients, data architects, and cross-functional teams to resolve data issues, optimize workflows, and deliver consulting recommendations.
• Leveraged a diverse data engineering stack: Wherescape RED, Apache Airflow, AWS (RDS, S3, Snowflake), IBM Cloud, Tableau, Power BI, IntelliJ, Git, Jenkins, Wherescape 3D.

Aug 2018 – Feb 2021 · 2 yrs 6 mos · Pune, Maharashtra, India · On-site

• I developed a comprehensive big data and data lake management system, achieving a 92% data accessibility rate. I integrated this system with Qlik Sense and Tableau to enhance data visualization and accessibility.
• I planned and implemented a CI/CD pipeline using Jenkins shared libraries, which led to a 95% deployment success rate and streamlined software releases by reducing manual intervention.
• I prepared and processed large raw datasets and redesigned Spark-powered dashboards in multi-layered production platforms, resulting in an 85%-95% improvement in data quality and decision-making.
• I automated project deployment using Control-M, reducing manual errors in software releases by 75% and improving the efficiency of the deployment process.
• I utilized programming languages such as Python, SQL, R, and SparkSQL, along with cloud platforms AWS and Azure, to manage and analyze data effectively.
• I leveraged big data technologies including Hadoop, Apache Spark, Hive, and Snowflake for large-scale data processing and storage. I also integrated Apache Airflow and Kafka for workflow management and data streaming.
• I employed Spark MLlib for predictive modeling and data mining, enhancing the capability to derive actionable insights from complex datasets.
• I managed and optimized databases such as Microsoft SQL Server, MySQL, PostgreSQL, MongoDB, and Amazon RDS, and utilized Azure Data Lake and HDFS for efficient data storage.
• I created interactive dashboards and reports using Tableau, Power BI, and Excel to provide comprehensive data insights and support business decisions.
• I utilized tools like IntelliJ for development, Git and GitHub for version control, and Jira and Confluence for project management and documentation.

Jun 2017 – Aug 2018 · 1 yr 2 mos · Pune, Maharashtra, India · On-site

• In my role as an Associate Engineer at Tata Communications, I built and optimized large-scale data platforms, integrated Azure services, and optimized database performance. I leveraged Azure Analytics tools to design robust data models and visualizations, translating business problems into clear requirements. My contributions led to improved data security, reduced storage expenses, and enhanced data-driven decision-making.