Sravani Nadimpalli

About Sravani Nadimpalli

Results-driven Senior Business Analyst with over 9+ years of extensive experience delivering enterprise-scale analytics across diverse domains. Partnered with executive stakeholders, engineering teams to design cloud-based data platforms, governed data models, self-service BI ecosystems supporting strategic, operational decision-making. Leveraged advanced SQL, Python-based analytics, modern BI tools to translate datasets into actionable insights, optimize business processes, drive measurable performance improvements. Designed, administered and optimized analytical warehouses to support transaction data, operational metrics; applied partitioning, clustering, distribution strategies to improve query latency, concurrency, cost efficiency for downstream reporting, BI workloads. Engineered complex SQL transformation layers to enable SLA monitoring, KPI computation, operational analytics; implemented performant joins, and incremental aggregation logic to surface reliable business metrics for finance, risk, and operations stakeholders. Built Python-based data processing pipelines to automate data ingestion, cleansing, validation from APIs, internal systems; standardized schema management and data quality checks to deliver trusted, analytics-ready datasets consumed by reporting, ML teams. Designed and governed semantic data models across Looker, Power BI, Tableau, standardizing business metric definitions, enabled scalable self-service analytics through dashboards that allowed non-technical stakeholders to analyze trends without direct SQL access. Engineered cross-cloud data ingestion and transformation pipelines across GCP, Microsoft Azure, and AWS, integrating data from transactional systems, APIs, and cloud storage while enforcing schema consistency, data validation, and lineage traceability. Built and operated production-grade data workflows using Azure Data Factory, orchestrating scheduled and event-driven ETL processes with robust error handling, retry logic, and pipeline monitoring to ensure reliable downstream analytics consumption. Implemented Delta Lake–based data architectures for enterprise datasets, managing slowly changing dimensions, organized data into raw, curated, and analytics-ready layers to support reliable BI reporting and downstream advanced analytics use cases.

Experience

1. Business Data Analyst

   Visa

   May 2025 — Present · US

   Contributed for Design and deployment of a next-generation, cloud-native intelligence platform that proactively safeguards global payment system resilience while optimizing operational performance. This enterprise initiative focuses on unifying real-time transaction telemetry, infrastructure logs, and business metrics into a single source of truth. The platform will automate the detection of anomalies and latency outliers, predict potential service degradation, and provide granular, real-time visibility into system health and financial transaction SLAs. By implementing a governed metrics layer and self-service analytics, it will empower both technical and business teams to ensure uninterrupted service, maintain compliance, and drive data-informed operational strategies, directly supporting the client\'s critical need for fault-tolerant, scalable, and secure financial network operations.Engineered SQL transformations, analytical queries in BigQuery, leveraging window functions, nested queries, BI Engine optimizations to deliver high-performance SLA, KPI analysis, supporting real-time operational monitoring enterprise decision-making.Designed materialized views, partitioned, and clustered tables in BigQuery to optimize query performance, streamline access to multi-source operational datasets, and support high-volume SLA and KPI analytics for enterprise monitoring.Built Looker semantic models, metrics layers with versioned dashboards, derived measures, reusable calculations, enforcing enterprise-wide KPI consistency, governance compliance, reliable cross-team analytics for operational, financial decision-making.Automated end-to-end data ingestion, preprocessing, and transformation pipelines using Python (Polars, Pandas) for data from cloud storage, APIs, and telemetry sources, enabling operational readiness and real-time analytics.

Education

• Vignan Institute of Technology and Science

  Bachelor of Technology - BTech, Computer Science