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Pradha - Power BI, Data Analyst, H1B, genuine profile, open for onsite, ready for face to face

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Location: Dallas, Texas, USA

Relocation: Yes

Visa: H1B

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Pardha Email: [email protected] +1 972-924-5835 (Employer), C2C Only, H1B (Passport Number also shared) Open to relocate anywhere in the USA Summary Accomplished Senior Analytics Lead with 10+ years of experience delivering enterprise-grade data engineering, business intelligence, and analytics solutions across Life Sciences, Healthcare, Retail, and Supply Chain domains, enabling data-driven strategic decision-making. Extensive expertise in designing and implementing end-to-end data pipelines, covering data ingestion, transformation, modeling, and visualization, using modern tools such as Azure Data Factory, Databricks, Snowflake, and Power BI. Proven ability to architect scalable and high-performance cloud-based data platforms leveraging Azure and AWS ecosystems, ensuring reliability, performance, and cost optimization. Strong hands-on experience with PySpark and Python for building distributed data processing frameworks, enabling efficient handling of large-scale structured and semi-structured datasets. Deep understanding of modern data architecture patterns, including Medallion Architecture (Bronze, Silver, Gold layers) and Lakehouse architecture, ensuring scalable and governed data environments. Advanced proficiency in data modeling techniques, including Star Schema, Snowflake Schema, and dimensional modeling, to support high-performance analytics and reporting systems. Expertise in developing interactive and insightful dashboards using Power BI, Tableau, Amazon QuickSight, and Qlik, with strong focus on data storytelling and executive reporting. Strong command over DAX, Power Query (M), and SQL optimization techniques, enabling development of complex business metrics, KPIs, and high-performing data models. Demonstrated experience in integrating enterprise data sources such as SAP (ERP), CRM systems (Salesforce/Veeva), MES, and WMS, creating a unified single source of truth for analytics. Proven track record of improving system performance by implementing query optimization, indexing, partitioning, and caching strategies, significantly reducing data latency and report refresh times. Hands-on experience in implementing CI/CD pipelines using Azure DevOps, enabling automated deployments, version control, and environment consistency across DEV, QA, UAT, and PROD. Strong expertise in implementing data governance, security, and compliance frameworks, including Row-Level Security (RLS) and role-based access control (RBAC). Experienced in applying advanced analytics techniques, including predictive analytics, forecasting, and statistical analysis, using tools like Python and SAS to generate actionable insights. Proven leadership capabilities in managing cross-functional teams, mentoring developers, collaborating with senior stakeholders, and driving enterprise analytics strategy and digital transformation initiatives. Technical Skills Data Platforms and Warehousing Snowflake, Azure Synapse Analytics, Databricks (Delta Lake), Dremio Databases Microsoft SQL Server, Oracle, MySQL, MS Access ETL / ELT and Data Integration Azure Data Factory (ADF), SQL Server Integration Services (SSIS) Programming and Query Languages Python, PySpark, SQL, DAX, Power Query (M) Business Intelligence and Visualization Power BI, Tableau, Amazon QuickSight, Qlik Sense, QlikView Cloud Platforms Microsoft Azure, Amazon Web Services (AWS) Data Modeling and Architecture Dimensional Modeling, Star Schema, Snowflake Schema, Medallion Architecture (Bronze/Silver/Gold) DevOps and Deployment Azure DevOps, CI/CD Pipelines, Git-based Version Control Automation and Low-Code Platforms Power Apps, Power Automate Analytics and Statistical Tools SAS (Data Analysis and Statistical Modeling) Experience Senior Analytics Lead at Gilead Sciences Inc from Jan 2024 to Present Project: Global Supply Chain Analytics Platform Description: End-to-end design and implementation of a cloud-based analytics platform integrating SAP, MES, and WMS systems to enable real-time supply chain visibility and KPI-driven decision-making. Led the design and implementation of a scalable enterprise data architecture by integrating multiple upstream systems such as SAP (ERP), MES, WMS, and third-party logistics platforms, establishing a unified and governed analytics ecosystem that serves as a single source of truth for global supply chain operations. Architected a modern lakehouse platform using Databricks and Microsoft Fabric, implementing Medallion Architecture (Bronze/Silver/Gold) to standardize data processing and enable scalable, governed analytics. Implemented Microsoft Fabric leveraging OneLake and Direct Lake mode, eliminating data duplication and reducing data movement across Azure Data Factory and Snowflake layers, resulting in a more unified and cost-efficient lakehouse architecture. Engineered high-performance PySpark pipelines reducing data latency by ~90% (24 hours to <2 hours), enabling near real-time supply chain decision-making. Optimized Snowflake architecture using clustering and partitioning, improving query performance by 60%+ and reducing compute costs by ~30%. Engineered robust ETL/ELT workflows using Azure Data Factory, orchestrating complex data workflows, managing dependencies, and enabling seamless integration between on-premises and cloud-based data sources. Developed interactive and highly intuitive dashboards using Power BI and Amazon QuickSight, incorporating advanced DAX calculations, KPI frameworks, and drill-through capabilities to provide real-time visibility into supply chain metrics such as inventory levels, order fulfillment rates, and lead times. Led the strategic migration of legacy BI platforms including QlikSense and Tableau to Amazon QuickSight, ensuring minimal disruption while improving scalability, maintainability, and cost efficiency. Automated repetitive and manual data processing tasks using Python scripting, improving operational efficiency and reducing manual effort by over 40%, while enhancing data accuracy and consistency. Implemented comprehensive data governance and security frameworks, including Row-Level Security (RLS) and role-based access control (RBAC), ensuring secure and compliant access to sensitive business data. Established CI/CD pipelines using Azure DevOps, enabling automated build, testing, and deployment processes across multiple environments, significantly improving release cycles and reducing deployment risks. Collaborated closely with business stakeholders and senior leadership to define analytics strategy, KPIs, and reporting frameworks, ensuring alignment between technical solutions and business objectives. Delivered actionable insights through advanced data storytelling and visualization techniques, helping business teams identify inefficiencies in procurement, logistics, and inventory management, ultimately improving operational performance. Provided technical leadership by mentoring team members, conducting code reviews, and establishing best practices in data engineering, BI development, and performance optimization. Continuously monitored and optimized system performance by implementing query tuning, caching mechanisms, and data model optimization strategies, ensuring high availability and responsiveness of analytics platforms. Environment: Microsoft Fabric (OneLake, Direct Lake), Azure Data Factory, Databricks (PySpark, Delta Lake), Snowflake, Power BI, Amazon QuickSight, Azure DevOps, SQL, Python Technology Lead / Power BI Lead at SK Life Sciences from Nov 2021 Dec 2023 Project: Pharma Sales & Clinical Analytics Solution Description: Design and development of an enterprise analytics platform integrating sales, clinical, and CRM datasets to support commercial performance tracking and regulatory analytics. Spearheaded the development of enterprise-level analytics solutions for pharmaceutical sales and clinical trial data by integrating multiple data sources, including CRM systems, ERP platforms, and clinical databases, into a unified reporting framework. Designed and implemented scalable data ingestion pipelines using Azure Data Factory, facilitating efficient data movement into Azure Data Lake, structured across raw and curated layers to support downstream analytics. Developed high-performance data models using Snowflake and Databricks, enabling advanced analytics, improved query performance, and seamless integration with BI tools. Built comprehensive and interactive dashboards using Power BI, incorporating advanced DAX functions, time intelligence calculations, and KPI tracking mechanisms, enabling stakeholders to monitor key metrics such as revenue, market share, and product performance. Led the migration from Qlik-based reporting systems to Power BI, modernizing the analytics ecosystem and improving user adoption, scalability, and maintenance efficiency. Automated workflows using Python and Power Automate, reducing manual effort by ~40% and improving data processing efficiency. Conducted advanced ad-hoc and predictive analytics, including forecasting and trend analysis, to support strategic decision-making and regulatory compliance requirements. Integrated CRM data (Salesforce/Veeva) with sales and operational datasets to enable enhanced customer segmentation, targeting, and campaign analysis. Optimized Power BI data models and queries, improving dashboard performance by ~60% and reducing report load times significantly. Implemented secure and scalable data access frameworks using Power BI Service, Gateways, and Row-Level Security (RLS), ensuring compliance with data governance policies. Collaborated with cross-functional teams including sales, marketing, and supply chain to translate complex business requirements into scalable technical solutions and analytics models. Delivered executive-level presentations using data storytelling techniques, enabling leadership to make informed decisions based on actionable insights. Environment: Azure Data Factory, Azure Data Lake, Databricks, Snowflake, Power BI, Python, Power Automate, SQL, Salesforce/Veeva CRM Tech Lead/Power BI Lead at Sanofi Inc from Mar 2021 to Oct 2021 Project: Healthcare Data Analytics & Reporting System Description: Development of a centralized analytics and reporting solution integrating EHR, financial, and operational data to enable standardized reporting and business insights. Led the development of enterprise-wide analytics and reporting solutions by integrating data from diverse healthcare systems including Electronic Health Records (EHR), financial systems, and CRM platforms, enabling a unified and reliable analytics ecosystem. Designed and implemented scalable ETL pipelines using Azure Data Factory, orchestrating data ingestion, transformation, and loading processes to support near real-time analytics and reporting. Built robust and scalable data models and semantic layers using Power BI and Azure SQL/Synapse, ensuring optimized performance and efficient querying for large healthcare datasets. Developed highly interactive dashboards using Power BI, leveraging advanced features such as drill-down, drill-through, bookmarks, and dynamic filtering, enabling users to explore data at multiple granularities. Implemented comprehensive Row-Level Security (RLS) and role-based access control (RBAC) mechanisms to ensure secure access to sensitive patient and financial data, aligning with compliance requirements. Applied advanced data transformation and cleansing techniques using Power Query (M) and SQL, ensuring high data quality, consistency, and reliability across reporting systems. Conducted advanced statistical analysis and A/B testing using SAS, enabling evaluation of marketing strategies, patient engagement initiatives, and campaign effectiveness. Designed and implemented automated reporting frameworks using Power BI Service, enabling scheduled refreshes, subscriptions, and report distribution to business users. Improved report performance by 50 60% through data model tuning, indexing, and query optimization techniques. Collaborated with cross-functional stakeholders to define KPIs, reporting standards, and data governance policies, ensuring alignment with business objectives. Delivered actionable insights through data visualization and storytelling techniques, enabling business users to make informed decisions related to patient care, operations, and marketing. Provided production support and troubleshooting for analytics solutions, resolving data discrepancies, performance bottlenecks, and system issues in a timely manner. Ensured adherence to data governance, compliance, and security standards, particularly in handling sensitive healthcare data. Mentored junior team members and established best practices in BI development, data modeling, and performance optimization, improving overall team productivity and solution quality. Environment: Azure Data Factory, Azure Synapse Analytics, Azure SQL, Power BI, Power BI Service, SQL, Power Query (M), SAS Senior BI Developer / Data Analyst at Biogen Inc from Oct 2020 to Feb 2021 Project: BI Modernization & Cloud Migration Initiative Description: Modernization of legacy BI systems through migration to Snowflake-based cloud architecture, improving scalability, performance, and data accessibility. Played a key role in modernizing legacy BI systems by designing and developing interactive QlikSense dashboards to support enterprise reporting and analytics requirements. Built efficient and scalable data models and ETL scripts using QlikSense scripting, ensuring optimized data processing and performance. Led data migration initiatives from Dremio to Snowflake, leveraging cloud-based architecture to improve scalability, performance, and data accessibility. Developed advanced data transformation logic to consolidate and standardize data from multiple heterogeneous sources, ensuring consistency across reports. Designed and implemented interactive visualizations using QlikSense features such as set analysis, master items, and advanced charting techniques. Collaborated with business stakeholders to gather requirements and translate them into technical specifications and BI solutions. Conducted detailed data analysis and validation, ensuring accuracy, completeness, and reliability of reporting datasets. Improved dashboard responsiveness by ~40% through data model optimization and query tuning. Developed and delivered ad-hoc reports and dashboards to support dynamic business needs and decision-making processes. Implemented best practices in BI development, including modular scripting, reusable components, and standardized naming conventions. Provided production support and resolved data issues, performance bottlenecks, and user-reported problems efficiently. Enhanced reporting capabilities by introducing new visualizations and improving user experience. Environment: Snowflake, Qlik Sense, Dremio, SQL, Data Modeling, ETL Pipelines Senior Software Developer at Cleartrip Pvt Ltd from Jan 2015 to Aug 2020 Project: Enterprise Data Warehouse & BI Platform Description: Design and implementation of an enterprise data warehouse and BI ecosystem supporting multi-source data integration, reporting, and business performance analytics. Designed and implemented enterprise-scale data warehouse solutions using QlikView and Power BI, enabling centralized reporting and analytics across multiple business functions. Developed robust ETL pipelines to extract, transform, and load data from diverse sources including Oracle, MySQL, Hive, Amazon Redshift, Cassandra, and flat files, ensuring seamless data integration. Built scalable dimensional data models using Star Schema and Snowflake Schema, supporting efficient querying and reporting for large datasets. Created optimized QVD-based data pipelines in QlikView, significantly improving data load performance and reducing processing time. Implemented incremental data loading strategies to efficiently handle high-volume and historical datasets, minimizing system load and improving refresh performance. Developed interactive and user-friendly dashboards using QlikView and Power BI, providing insights into key metrics such as sales, revenue, discounts, and financial performance. Migrated legacy BI solutions from QlikView to Power BI, modernizing the reporting infrastructure and enhancing scalability and usability. Applied advanced DAX calculations and Power Query transformations to implement complex business logic and KPIs. Implemented Row-Level Security (RLS) to ensure secure and role-based access to sensitive business data. Automated business workflows using Power Automate (Microsoft Flow), enabling approval processes, notifications, and operational efficiency improvements. Optimized SQL queries and reporting performance using indexing, query tuning, and data model optimization techniques, ensuring faster data retrieval. Provided ongoing production support, maintenance, and enhancements for BI systems, ensuring reliability and availability. Environment: QlikView, Power BI, Oracle, MySQL, Hive, Amazon Redshift, Cassandra, SQL, Power Automate CERTIFICATIONS Microsoft Certified: Power BI Data Analyst Associate Tableau Desktop Specialist Certification Keywords: continuous integration continuous deployment quality analyst business intelligence active directory microsoft mississippi