May 2018 – Feb 2021 · 2 yrs 9 mos · Remote

• ♦Helped architecture and development teams by creating an ETL framework leveraging Databricks and Snowflake, leading to a scalable and efficient data solution that improved data consistency by 30% and enabled faster decision-making.
• ♦Led the design and deployment of an enterprise-wide Databricks Lakehouse solution to unify real-time and batch data sources, reducing data latency by 24% and boosting the analytics team's efficiency.
• ♦Collaborated closely with stakeholders to align business requirements with Snowflake architecture, optimizing storage and retrieval strategies that improved query performance.
• ♦Directed the integration of Databricks and Snowflake for seamless data ingestion and transformation, resulting in zero downtime during critical migrations and automated quality checks that decreased errors by 25%
• ♦Mentored data engineering teams on best practices for Lakehouse architectures and CI/CD workflows, improving deployment success rates by 38% and establishing a knowledge-sharing culture.
• ♦Developed and automated Python-based scripts in Databricks to monitor and validate data quality, reducing manual checks by 42% and improving overall data governance practices.
• ♦Leveraged PySpark in Databricks to build scalable data transformation pipelines, enabling real-time processing of large datasets and decreasing batch window times by 24%
• ♦Designed dynamic ETL pipelines in Azure Data Factory (ADF) using parameterized datasets and linked services, improving pipeline flexibility and reducing deployment times by 25%
• ♦Resolved a critical data integrity issue by designing a custom Databricks workflow using Python and PySpark to identify and correct missing or inconsistent data in real-time, reducing data discrepancies by 54% and ensuring accuracy for high-impact business reports.
• ♦Developed interactive dashboards in Tableau and Power BI, consolidating multiple data sources and delivering actionable insights that improved executive decision-making

Feb 2016 – May 2018 · 2 yrs 3 mos · Remote

• ► Optimized SQL-based ETL jobs for Microsoft’s data lake using Databricks and Azure Synapse, decreasing job run times by 45% while improving data consistency across multiple global teams.
• ►Designed an Azure-based Lakehouse architecture leveraging Databricks Delta Lake to consolidate diverse data sources, enabling the BI team to generate reports 50% faster and deliver real-time analytics to executives.
• ►Implemented DataOps practices in Azure DevOps to streamline deployment pipelines and automate testing, reducing data processing errors by 35% and improving the reliability of product releases.
• ►Managed a cross-functional team in implementing Databricks Delta Lake, which consolidated various raw data sources into a unified architecture, leading to a 50% reduction in data silos and increased collaboration between the data science and BI teams.
• ►Developed Python scripts and Databricks workflows for data pipeline automation, which increased data ingestion speeds by 40% and significantly reduced manual intervention for the Data Engineering team.
• ►Collaborated with stakeholders to define CI/CD workflows using Terraform and Azure DevOps, enhancing deployment success rates by 50% and mentoring junior engineers on best practices.
• ►Devised a scalable ETL framework using Databricks, SQL, and PySpark, which processed high-volume gaming telemetry data, improving anomaly detection by 25% and contributing to better customer experience management.
• ►Led efforts to establish cross-team documentation standards and create centralized knowledge repositories, increasing efficiency in collaborative projects by 20%.
• ►Led the design and deployment of a Databricks Lakehouse solution to integrate streaming and batch data, reducing pipeline complexity by 30% and enabling real-time data analytics across multiple departments.
• ►Developed an enterprise-wide framework using Databricks and Snowflake, which enabled scalable ETL processing of large datasets, improving reporting speeds by 35%

Jul 2012 – Feb 2016 · 3 yrs 7 mos

• Transitioned from SQL Developer to Data Engineer by optimizing SQL queries, developing Python-based automation, building ETL pipelines in SSIS, creating data visualizations, and leveraging AWS for scalable cloud solutions.
• ⦿Optimized SQL stored procedures to improve query performance, reducing data processing time by 35% and enhancing the efficiency of data-driven reports.
• ⦿Developed and maintained ETL pipelines using SSIS, integrating large datasets from multiple sources, which increased data reliability and processing speed by 30%.
• ⦿Designed and implemented SSRS reports, delivering real-time analytics to key stakeholders and enabling quicker data-driven decisions for client projects.
• ⦿Built multidimensional cubes and tabular models with SSAS, improving the reporting performance and allowing faster drill-down analysis for business users.
• ⦿Created interactive Power BI dashboards, providing clients with visual insights that helped them identify key performance indicators and make informed decisions.
• ⦿Migrated on-premise databases to AWS RDS, improving database availability and reducing maintenance costs by 20% through AWS services.
• ⦿Conducted data analytics on large-scale datasets to uncover trends and provide actionable insights, leading to optimized marketing strategies and 15% increase in ROI for client campaigns.
• ⦿Mentored junior team members on SQL best practices, ETL processes, and BI tool usage, enhancing team productivity and ensuring high-quality deliverables for clients.
• ⦿Architected a scalable data pipeline on AWS using Lambda, Glue, and S3, enabling automated ingestion and transformation of real-time streaming data, reducing processing latency by 40% and improving overall system resilience.
• ⦿Optimized database infrastructure by migrating on-prem SQL servers to AWS RDS with automated backup and failover configurations, achieving 99.9% uptime and reducing maintenance costs by 25% through efficient resource allocation.