About Me
Results-driven Data Engineer with over 5+ years of experience specializing in building scalable data solutions across Azure, AWS, Microsoft Fabric, and Databricks. Proven expertise in designing end-to-end ETL/ELT pipelin…
Results-driven Data Engineer with over 5+ years of experience specializing in building scalable data solutions across Azure, AWS, Microsoft Fabric, and Databricks. Proven expertise in designing end-to-end ETL/ELT pipelines, optimizing complex SQL queries, and implementing robust lakehouse architectures. Successfully migrated legacy data processes to modern cloud platforms and delivered granular, high-impact business metrics. Adept at leveraging PySpark for real-time analytics and automating infrastructure with CI/CD methodologies to drive enterprise business intelligence.
Experience
Analyst - RNA
<p>Migrated complex, high-volume business queries to Microsoft Fabric, developing optimized stored procedures that significantly reduced report generation time and enhanced data accessibility.</p><p> Engineered and maintained mission-critical data pipelines, resolving complex SQL join fan-out issues and ensuring accurate cumulative calculations for core reporting.</p><p> Collaborated with cross-functional stakeholders to deliver comprehensive, granular business metrics and KPI dashboards for annual performance reviews.</p><p> Designed and implemented AWS-based data pipelines to extract data from Amazon Redshift and load it into Amazon S3, powering downstream analytics.</p><p> Constructed a configuration-driven AWS Glue ETL framework orchestrated via Step Functions, supporting full and incremental data loads to improve scalability.</p><p> Implemented metadata-driven pipeline architectures using reusable PySpark scripts, dynamically handling multiple tables and significantly reducing the development lifecycle.</p>
Data Engineer
<p>Architected and managed robust ETL pipelines using Python and PySpark for scalable ingestion and processing of large datasets from disparate sources.</p><p> Developed streaming applications using PySpark Structured Streaming to provide real-time calculations, improving alerting capabilities and system responsiveness.</p><p> Optimized complex SQL queries for data analysis and aggregation, ensuring fast and accurate retrieval of analytical datasets.</p><p> Automated Azure infrastructure provisioning using Terraform, deploying Databricks workspaces, clusters, and SQL Warehouses.</p><p> Established CI/CD pipelines in Azure DevOps, facilitating the smooth, automated deployment of data pipelines and infrastructure components.</p>
Programmer Analyst Trainee
<p>Engineered a generic, metadata-driven data quality framework using PySpark to standardize and validate data via JSON configuration files.</p><p> Built cloud-native ETL pipelines utilizing AWS, SQL, Python, and PySpark to process high-volume enterprise data.</p><p> Automated secure data ingestion from SFTP to AWS S3 buckets utilizing Paramiko and Boto3 Python libraries.</p><p> Managed source code within GitHub repositories and configured Jenkins pipelines to seamlessly migrate code across testing and production environments.</p>
System Engineer Trainee
<p>Developed and orchestrated data pipelines in Azure Data Factory (ADF) to extract data from multiple sources and load it into Azure Synapse Analytics.</p><p> Transformed complex datasets using PySpark and orchestrated the loading of processed data into Azure Data Lake Storage (ADLS).</p><p> Built a comprehensive data warehouse incorporating diverse facts and dimensions, executing data transformations with Spark SQL.</p><p> Authored PySpark functions in Jupyter for rigorous data validation, including duplicate record elimination and type validation.</p>