Rushik Shah

Client works on ITSM domain and built ITIL 2011 Certified solution that unifies service management, asset management, and operations management in a single, easy-to-use offering.

• Designing scalable and high-performance data architecture based on client business and technical requirements.
• Developing and orchestrating end-to-end data pipelines using Azure Data Factory (ADF) for extracting, transforming, and loading structured and semi-structured data into Azure Synapse Analytics and Snowflake Cloud Data Warehouse.
• Implementing Medallion Architecture (Bronze, Silver, Gold layers) using Azure Databricks (PySpark) to support scalable, reliable, and optimized big data processing.
• Building and managing workflow orchestration using Apache Airflow for scheduling, monitoring, and managing dependencies across batch and near real-time data pipelines.
• Designing and optimizing data models using Data Vault 2.0 methodology, ensuring scalability, auditability, and historical tracking in the data warehouse.
• Designing and deploying CI/CD pipelines using Azure DevOps, automating build, test, and deployment processes for data pipelines and Databricks notebooks.

Client is one of the largest financial institutions in terms of market capitalization, earnings and customers. Client wanted to build a custom data analytics platform for their business needs.

• Architected and implemented end-to-end data ingestion frameworks to load structured and semi-structured data into a Data Vault architecture (Raw Vault & Business Vault) based on enterprise reporting and analytics requirements.
• Designed and developed scalable Airflow DAGs and AWS Glue (PySpark) jobs to ingest data from multiple sources including RDBMS (MS SQL Server, PostgreSQL), streaming systems, ERP platforms, and external APIs into cloud data lakes (Amazon S3).
• Modeled and implemented Data Vault 2.0 structures including Hubs, Links, and Satellites, ensuring historical tracking, auditability, and scalability of enterprise data assets.
• Implemented CI/CD pipelines using Azure DevOps and Jenkins to automate deployment of ETL workflows, Airflow DAGs, SQL scripts, and infrastructure configurations.
• Established data lineage and impact analysis using Oracle Enterprise Data Catalog (EDC), ensuring governance, traceability, and compliance across the data ecosystem.

The global leader in crew and workforce accommodations wanted to streamline and consolidate the Analytics platform.

• Developed high-performance ETL/ELT pipelines using AWS Glue (PySpark) to ingest and process data from diverse sources such as MS SQL Server, PostgreSQL, Salesforce, and RESTful APIs.
• Designed and implemented Snowflake-based cloud data warehouse architectures, applying Data Vault and dimensional modeling principles to support historical tracking and analytical scalability.
• Built enterprise-grade BI reporting solutions using AWS QuickSight and Power BI, converting large datasets into meaningful dashboards and decision-support insights.
• Optimized Glue PySpark workloads by implementing predicate pushdown, broadcast joins, partition tuning, and memory configuration, significantly improving runtime performance.

The HealthCare Company integrates with any health insurance carrier or employer-sponsored plan and automatically brings together both lifestyle and disease management data and tools into a personalized health portal accessible on any mobile device or Internet web browser.

• Architected and developed end-to-end ETL pipelines using Talend, integrating heterogeneous data sources including CSV, flat files, HL7 healthcare formats, Salesforce CRM, and relational databases into standardized canonical models.
• Designed and implemented scalable data ingestion frameworks to migrate high-volume transactional data into an Enterprise Data Warehouse (Amazon Redshift), enabling structured analytics and reporting.
• Built and orchestrated data workflows using Apache Airflow, automating dependency management, job scheduling, monitoring, and failure recovery across multiple data pipelines.
• Developed Talend ETL solutions for Salesforce CRM data processing, enabling customer analytics, operational reporting, and cross-platform data synchronization.

DRive is a revenue analysis product. Talend ETL is used to load data in terms of master data like customer, product etc and transaction data like, chargeback, rebate, direct sales etc to SQL and Hadoop systems.

• Designed and developed end-to-end ETL pipelines using Talend to ingest, transform, and load large volumes of structured and semi-structured data from multiple source systems into the Enterprise Data Warehouse.
• Built scalable data processing applications using Hadoop Map Reduce, enabling distributed processing of high-volume datasets and improving batch processing efficiency.
• Modeled and implemented Data Vault architecture (Hubs, Links, Satellites) to support scalable, auditable, and historical data warehousing solutions.
• Configured and managed Talend MDM data models, ensuring master data consistency and governance across enterprise systems.

• Designed and developed scalable data pipelines using the Hadoop ecosystem to process large volumes of structured and semi-structured data from multiple enterprise sources.
• Engineered end-to-end data integration workflows between Hadoop platforms and MS SQL Server to enable centralized analytics and reporting.
• Modeled enterprise data using Data Vault architecture, including Hubs, Links, and Satellites, ensuring scalability, auditability, and historical tracking.

• Designed and developed scalable web applications using C# and .NET framework, integrating backend services with MS SQL Server to support high-volume transactional systems.
• Built and maintained robust ETL pipelines using SSIS, enabling efficient extraction, transformation, and loading of structured and semi-structured data from multiple data sources.
• Performed advanced query optimization and execution plan analysis to improve database performance and reduce latency in data processing operations.

• Designed and developed scalable web applications using C#, ASP.NET, AJAX, JavaScript, CSS, and SQL Server 2005, ensuring high performance and optimized data handling capabilities.
• Engineered and optimized complex SQL queries, stored procedures, views, triggers, and indexes to improve database performance, reduce latency, and support high-volume transactional systems.
• Architected and maintained data models and relational database schemas, ensuring data integrity, normalization, and efficient storage design aligned with business requirements.