Abhishek Saxena

Developed monitoring and alerting capabilities using Amazon CloudWatch and AWS SNS to send out automated emails using BOTO3 to ensure 100% of data pipelines were working and improved the uptime of the network by 18% year over year. Built and automated efficient ETL processes (3+ billion rows of data) using Airflow and Redshift that saved the company $240K annually, it improved conversion rates by 16% and reduced manual workload by 34% monthly. Developed and maintained a key data pipeline to process over 400 TB of structured and semi-structured data, consolidating it from multiple disparate sources into a single destination using Redshift, S3, Python and Athena. This pipeline enabled quick data analysis for reliable business insights and identified potential fraudsters, preventing a $1.6M loss. Drafted, updated and shared 10+ functional and technical specification documents stipulating the business requirements that can be used by developers to implement new features or enhancements. Created consistent data sources by performing operations such as cleaning, transformation, validation, aggregation and distribution of data from 40+ sources and ensuring integrity in a relational environment by working closely with the stakeholders & solution architect and were used by the data science team to create machine learning models, resulting in a 36% increase in revenue. Designed, developed, deployed and maintained data services for 15+ pipelines using AWS services such as Kinesis, S3, Athena, Redshift to process petabyte-scale data in real-time. Led the migration from Oracle to Redshift using AWS DMS, resulting in an annual cost savings of $634K and an increase in performance of 16%. Expanded and optimized data and data pipeline architecture, increasing efficiency by 30%. Delivered 25+ Big Data requests to increase data correctness, quality and completeness, thereby allowing decisions made from Big Data analysis to occur faster. Interacted with business partners, business anal

Worked on leveraging the supply chain data using ETL pipelines built via SQL and Azure ADF, helping the sales leadership team in quick and impactful decision-making. Assisted in modifying algorithms and business models for the supply chain data to find out weak areas and solve business problems. Using the A/B testing method determined strategic marketing opportunity for the client through analysis, resulting in savings of $54K in the annual campaign budget. Automated the daily data pipeline process using Python, Azure functions and logic apps saving about ~900 human hours/year devoted to updating dashboards. Redefined existing recommendation of marketing program from a traditional rule-based system to a more AI-Driven approach which resulted in value addition of a total of $2.3M. Performed Exploratory Data Analysis on the supply chain data and analyzed market and competitor behavior, which increased forecasting accuracy by 16%. Generated reports and visualized them graphically using Power BI. Worked in a collaborative Agile environment with 20+ global technology professionals including product and data science teams on multiple projects including idea generation, implementation, testing and success measurement.