• Developing and Maintained Enrich layer in SQL server for Marketing Analytics Team.• Experience working with cross-functional teams distributed across the globe.• Assisting solution providers with the definition and implementation of technical and business strategies.• Primarily Involved in Data Modelling in PowerBI and Maintaining workspaces and deployment pipelines.• Helping team with Automating tasks using Python and SQL.• Designing and implemented scalable data pipelines using Apache Spark and Python to process and analyze large datasets, improving data processing speed by 40%.• Led the migration of the company’s on-premises data warehouse to AWS Redshift, optimizing storage costs and query performance by 30%.• Tune and Troubleshoot Snowflake for performance and optimize utilization.• Integrating diverse healthcare data sources, including EHRs, claims data, and patient demographics, into a centralized data warehouse, enabling comprehensive data analysis and reporting.• Implementing data quality checks and governance frameworks using Apache NiFi and Talend, ensuring high data integrity and compliance with industry regulations like HIPAA

• Implemented data pipelines using Spark, Hive, and Kafka to process, analyze, and transform large volumes of structured and unstructured data in Hadoop ecosystem.• Ensured that the Data Vault architecture is scalable to handle increasing data volumes and changing business requirements.• Created snowflake tasks for various types of schedules. Creating Complex SQL Queries using Views, Indexes, Triggers, Roles, Stored procedures, and User Define Functions.• Foster a culture of collaboration and knowledge sharing between teams working with DBT and Data Vault.• Manage resources, timelines, and budgets for Data Vault projects.• Design and implement the overall data architecture, considering the principles of Data Vault and how it fits into the broader data landscape.• Utilize DBT to run queries and perform data analysis on transformed datasets. Leverage DBT's documentation features to document the purpose, business logic, and context of datasets.• Develop and maintain SQL transformations using DBT to create analytical datasets.• Designed and developed data ingestion pipelines using Nifi and Sqoop to move data between Hadoop and other data systems like AWS S3, Azure Blob storage, and Redshift.• Created and maintained data workflows using Apache Airflow to schedule and monitor ETL jobs, ensuring data quality and accuracy. Involved in migrating the live project to AWS cloud environment by using various services like Lambda function, Step function, S3, Glue, Redshift, EC2, CloudWatch etc.,• Working on architecting, designing, and implementation of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse. Implemented New ETL Solution based on Business Requirements. Work with source system owners to integrate new data sources into the Data Vault

Design and implement data models following the principles of the Data Vault methodology, including Hubs and Links. Provide training to team members on the principles and best practices of DBT and Data Vault.• Design of workflow which includes setting up DEV, QA, and PROD environments, creating users, and managing their permissions. Copy activity, Custom ETL Pipeline Activities for On-cloud ETL processing using various source systems like SAP, Salesforce, Various enterprise-level applications.• Provided pivotal technical insights for business requirements, liaising with Business Analysts, Client teams, and Development units. Developed scripts (Python) to Extract, Load, and Transform data.• Collaborated closely with stakeholders to define integrations, features, and partnerships. Engineered data pipelines in Azure Data Factory (ADF), orchestrating ETL processes from diverse sources to Azure SQL, Blob storage, and Azure SQL Data Warehouse.• Spearheaded Data Migration leveraging SQL, SQL Azure, Azure storage, and Azure Data Factory, including SSIS integration. Transformed data into insightful reports within Analytics team fostering informed decisionmaking. Designed Power BI dashboard reports aligned with business requirements, enhancing data visualization.• Optimized data collection, analysis, and visualization methods for improved efficiency.• Migrated data from on-premises databases (SQL Server) to Cloud databases/Snowflake.• Implemented snow pipes to auto ingest data from various file formats (Parquet, JSON, CSV) to snowflake tables.• Experience in Migrating MS SQL server, Teradata objects into Snowflake environment.• Implemented timestamp-based CDC and SCD type1 and type2 using Matillion pipelines to capture delta in snowflake. Developed Spark applications using Pyspark and Spark-SQL for data extraction, transformation and aggregation from multiple File formats.