Automated ETL/ELT processes using AWS Glue and Lambda, and improving data ingestionand pipeline performance by 20%.• Achieved a 7% cost reduction while presenting stakeholders with a comprehensive PowerBIdashboard, aligning expansion strategies with risk mitigation goals.• Demonstrated a solid understanding of data warehousing concepts, OLTP and OLAP datamodels, and applied basic transformations to enhance data flow efficiency.• Created and maintained detailed documentation for data assets, communicating complex businessproblems and analytics requirements to both technical and non-technical stakeholders.

(July 2021 - October 2022 )Dynamic Module Lead with a track record of optimizing data management for a leading global investment management company. Proficient in PySpark and SQL, consistently driving cost savings and performance enhancements.•Spearheaded the migration of a vast array of tables, an extensive network of pipelines, andnumerous datasets from Azure HDInsight to Azure Gen1 and Gen2 datalake repositories,reducing costs by $10,000 (90%). Employed Microsoft PowerBI for powerful analytics,improving business insights.• Developed and monitored a multitude of end-to-end Azure Data pipelines, automating ETL/ELT ingestion scripts, and document workflow process thereby reducing manualintervention by 80%. Conducted comprehensive performance evaluations, resulting in a 30%improvement in data processing speed and efficiency.• Developed Power BI dashboards integrating PySpark on Azure Databricks for informeddecision-making, blending sustainability KPIs and stock trends to offer investment consultancy,with data insights.• Experienced in Agile methodologies, statistical analysis, and familiarity with version controland 5 different relational databases(September 2020 - June 2021 )Developed and fine-tuned ingestion scripts deployed on AWS Glue, facilitating the efficient extraction and transformation of sales and after-sales data. Enhanced data accessibility by implementing AWS Athena for streamlined querying, resulting in accelerated insights generation and decision-making processes.• Developed and optimized AWS Glue ingestion scripts for efficient extraction and transformation of sales and after-sales data, improving data accessibility.• Collaborated cross-functionally to implement script optimizations, resulting in a 25% boost in data processing speed.• Implemented data governance protocols to ensure data integrity and compliance with industry regulations.

Experienced Data Engineer with a proven track record of optimizing data management systems for a global convenience store chain. Proficient in PySpark and SQL, consistently driving efficiency and performance improvements.• Enhanced data ingestion speed with PySpark and Azure and complex SQL queries, utilizingdelta operations for efficiency. Streamlined ETL/ELT pipelines, reducing run time by 20%through strategic map pool implementation.• Architected and managed ELT/ETL data ingestion pipelines and scripts consisting diverseingestion modes (e.g. append, overwrite, merge, SCD type 2), optimizing process and storage forPoint of Sales(POS) data of a global convenience retail chain situated in North America.• Created Reports and KPIs for business performance monthly, quarterly ,yearly that alignwith the design and maintenance of data models used for managerial reporting and analytics.• Enabled smooth production deployments of diverse scripts, mitigating downtime andoptimizing pipeline efficiency, accessible documentation for seamless data migration betweenDevelopment and Production environments.

Experienced intern proficient in PySpark, Spark, Hadoop, AWS Glue, Lambda, S3, with real-life project expertise.Demonstrated proficiency in PySpark, Spark, Hadoop, and AWS services (Glue, Lambda, S3) through hands-on real-life projects and diverse case studies, showcasing adaptability to complex data scenarios and problem-solving skills essential for impactful contributions in data-driven roles.