Sajan Kumar Email & Phone Number

United States

Chicago, Illinois, United States

- Leveraged 300GB of athlete data in SQL (GCP) for customer behavior analysis, impacting a $5.5M Footwear business.- Implemented DBT for streamlined data transformation.- Utilized Pivot Tables for rapid data analysis, generating actionable reports.- Designed a store complexity model with 91.5% accuracy, reducing HR time-to-hire by 30%.- Led a team in developing a store inventory tracking solution, boosting reporting efficiency by 40%.- Developed an Excel profit model, saving $3.3M in payroll costs.- Analyzed large datasets in Python for innovative business solutions.- Deployed machine learning models for efficient inference.- Communicated complex findings effectively using visualization tools.- Conducted Market Basket Analysis, increasing sales by 10%.- Excel proficiency for automation and analysis.- Provided data reports and conducted market research.- Applied advanced machine learning algorithms for prediction.- Partnered with compliance teams for anomaly detection.- Created dynamic Pivot Tables for strategic decisions.- Integrated JIRA with Confluence for traceability and initiated A/B Testing.- Mentored interns in storytelling skills.

Dallas, Texas, United States

• Utilized Pivot Table functionalities for comprehensive data subset analysis.• Enhanced 60TB data processing pipelines efficiency, reducing processing time by 30% and improving data accuracy by 25%.• Built ETL for big data transactions across GCP, Hadoop, and Vertica.• Conducted Joint Requirements Development sessions to analyze database requirements.• Developed data serialization spark common module for various formats.• Proficiently utilized Pivot Tables for dynamic multi-dimensional analysis and dashboard creation.• Excel expertise encompassing advanced functions for complex reporting needs.• Migrated on-premises solutions to GCP, reducing maintenance costs by 50%.• Implemented Lambda architecture using Kafka, Spark, and Cassandra.• Administered JIRA user permissions and conducted team training sessions.• Integrated Excel with external data sources through Power Query for enrichment.• Supported production jobs and improved job performance.• Designed and implemented machine learning solutions.• Created SQL procedures for business logic and enhancements.• Utilized Excel's Power Query for data transformation.• Designed real-time dashboards in Tableau for sales performance tracking.• Employed Data Validation techniques for Critical Data Elements.Environment: SQL, Python (NumPy/ Pandas/ Scikit-learn), R Studio, GCP (BigQuery/ GCS Bucket/ G-Cloud Function), CI/CD, Jupyter, JIRA, Unix, Alteryx, GitHub, Hadoop, ETL, Advanced Microsoft Excel, Microsoft Office Suite, Power BI.

Louisville, Kentucky, United States

• Created calculated fields and items in Pivot Tables for nuanced data analysis and visualization.• Collaborated with a data warehouse architect to enhance data quality by 20%.• Architected Data Marts and databases to improve query performance by 40%.• Utilized Spark SQL API in PySpark for data extraction and loading.• Employed Azure ML's scalable resources for efficient model training on large datasets.• Designed and deployed data dashboards using Microsoft tools, improving data analysis efficiency by 25%.• Custom process design of transformation via Azure Data Factory & Automation Pipelines.• Extensively utilized Azure services like Azure Data Factory and Logic App for ETL processes.• Analyzed ten years of customer behavior data through impactful visualizations built using Tableau and Python.• Leveraged Pivot Table functionalities to organize complex datasets hierarchically.• Architected large-scale BI solutions on Azure using Azure Data Platform services.• Integrated VLOOKUPs with named ranges to optimize data retrieval processes.• Designed end-to-end systems for Data Analytics and Automation with Power BI.• Highly knowledgeable in Data Warehouse schemas and modeling concepts.• Integrated JIRA with third-party tools to optimize project tracking and management.• Involved in data modeling and ingesting data into Cassandra.• Implemented a distributed stream processing platform for big data pipelines.• Identified recurring patterns or anomalies within the data for insights.Environment: Python, SQL, Azure (Azure Data Factory/ Azure Data Lake), PySpark, SSIS, Power BI, TensorFlow, Microsoft Excel, Apache Airflow, REST APIs.

Hyderabad, Telangana, India

• Generated ETL mappings and workflows to stack data from various sources, reducing migration time by 35 hours.• Automated metadata extraction and lineage tasks using Python, saving 70+ hours of effort.• Created dynamic Excel dashboards with Pivot Tables and charts for comprehensive data visualization.• Established S3 data sources for adhoc querying and dashboarding with QuickSight and Tableau.• Ensured 99.5% data accuracy across systems through data profiling and validation.• Executed Tableau dashboards for insights, increasing data-driven decision-making by 25%.• Combined VLOOKUPs with INDEX-MATCH for robust data retrieval, enhancing accuracy.• Improved customer delivery date accuracy by 55% using Machine Learning models.• Built AWS infrastructure and managed AWS services like EC2, S3, and Dynamo DB.• Implemented CI/CD tools like Jenkins for code repository and deployment.• Designed data models for AWS lambda applications and implemented Confluence page restrictions.• Managed JIRA configurations and developed custom JIRA reports and dashboards.• Implemented AWS lambda for serverless computing and utilized Spark for Hadoop optimization.• Utilized SageMaker for data preprocessing and developed Hadoop and Spark clusters on AWS EMR.• Collaborated with cross-functional teams for data analysis and presented insights to executives.• Used Python data science packages like Pandas, NumPy, and Scikit-learn extensively.• Designed and deployed AWS Redshift for optimal data storage and retrieval.• Developed analytical and statistical models as per organizational requirements.• Designed Tableau dashboards to track key performance indicators, reducing effort by 40%.• Streamlined data integration and applied Machine Learning techniques for product ads.• Facilitated cross-team collaboration by integrating Confluence with chat and messaging tools.