Senior Data & Business Analytics Professional

SUPRAJA

Data Analyst Email: ****************@*****.*** Mobile: +1-346-***-****

PROFESSIONAL SUMMARY

●Data Analyst / Business Analyst with 5+ years of experience designing and delivering enterprise-scale analytics platforms across Snowflake, Azure Synapse, AWS, and Power BI across supply chain, retail, and banking domains.

●Strong expertise in dimensional data modeling (star schemas, SCD Type 1/2, snapshot facts, conformed dimensions) enabling standardized KPI frameworks across enterprise reporting ecosystems.

●Experienced in building and optimizing large-scale SQL data pipelines and transformation frameworks processing 100M–1B+ records across Snowflake, Synapse, and AWS Athena with performance tuning and cost optimization.

●Proven track record in developing governed semantic layers and enterprise KPI frameworks using Power BI and DAX, driving consistent reporting across 100+ dashboards and 900+ data elements in regulated environments.

●Demonstrated business impact through advanced analytics including $18M inventory optimization, $4.1M transportation savings opportunity identification, and 20%+ improvements in operational and risk KPIs.

TECHNICAL SKILLS

Category

Skills

SQL & Programming

Advanced SQL, T-SQL, Window Functions, CTEs, Python (Pandas), Query Optimization

Cloud Data Platforms

Snowflake, Azure Synapse Analytics, AWS (S3, Glue, Athena), SQL Server

Data Warehousing

Dimensional Modeling, Star Schema, Snowflake Schema, SCD Type 1/2, Fact/Dimension Design

Data Analytics & Engineering

ETL/ELT Pipelines, Incremental Loads, CDC, Data Orchestration, Stream/Task Processing

BI & Visualization

Power BI, Tableau, Power BI Fabric, Tabular Models, Tableau Hyper, DirectQuery/Import Mode

Semantic & Metric Layer

Semantic Modeling, KPI Standardization, Data Marts, Business Glossary, Metric Governance

Performance Engineering

Query Tuning, Partition Pruning, Micro-partition Optimization, Materialized Views, Caching Strategies

Advanced Analytics

Time-Series Analysis, Cohort Analysis, Segmentation, Forecasting, Risk Modeling

Data Governance & Security

Data Lineage, Data Quality Frameworks, Metadata Management, RLS, OLS, Audit Controls

Enterprise Analytics Engineering

Distributed Data Architecture, Multi-domain Analytics Systems, Scalable Reporting Frameworks, Cross-functional Data Integration

PROFESSIONAL EXPERIENCE

Cardinal Health (Business Analyst II) Frisco, TX Mar 2025 - Present

●Architected a multi-domain analytical ecosystem across Snowflake, AWS S3, AWS Athena, AWS Glue, and Power BI Fabric, integrating inventory, procurement, transportation, warehouse execution, supplier management, and customer fulfillment domains into a governed analytics platform processing 8.7TB of daily operational data and supporting 500+ decision-makers.

●Established canonical business entities and conformed dimensions spanning Product, Supplier, Customer, Distribution Center, Shipment, and Inventory domains, eliminating duplicate metric calculations across 40+ reporting workstreams and enabling enterprise-wide KPI standardization.

●Designed dimensional warehouse structures utilizing SCD Type 1 and Type 2 strategies, bridge tables, degenerate dimensions, accumulating snapshot facts, and periodic snapshot models to support inventory lifecycle tracking, fulfillment velocity analysis, and supply chain performance measurement.

●Engineered Snowflake warehouse optimization strategies through query profile analysis, micro-partition pruning assessment, clustering-depth evaluation, automatic clustering governance, and warehouse concurrency tuning, reducing average dashboard refresh latency from 18 minutes to under 4 minutes across 120+ reporting assets.

●Developed reusable SQL transformation frameworks leveraging result-set caching, secure views, transient tables, streams, tasks, window functions, recursive CTEs, and incremental merge patterns supporting 650M+ inventory and logistics records while minimizing compute consumption.

●Implemented workload segmentation strategies across virtual warehouses by isolating ELT, ad hoc analytics, executive reporting, and data science workloads, increasing platform concurrency by 3.5x without additional warehouse scaling.

●Institutionalized enterprise data observability practices through automated completeness validation, distribution profiling, schema drift detection, referential integrity enforcement, anomaly monitoring, and reconciliation controls spanning 1,800+ critical data elements.

●Authored end-to-end lineage frameworks tracing analytical assets from SAP ERP source transactions through AWS ingestion pipelines, Snowflake transformation layers, semantic models, and Power BI reporting artifacts, reducing root-cause analysis efforts by 62%.

●Designed governed semantic models supporting inventory turns, order fill rates, demand variability, transportation cost per shipment, warehouse productivity, supplier OTIF performance, and inventory aging metrics, enabling consistent metric consumption across 60+ executive dashboards.

●Constructed advanced DAX frameworks utilizing calculation groups, context transition optimization, virtual tables, iterator functions, and dynamic measure branching techniques supporting multi-dimensional supply chain analysis across 15+ business functions.

●Implemented row-level and object-level security architectures governing access to 4,500+ operational attributes while preserving analytical flexibility for Procurement, Operations, Finance, and Executive Leadership teams.

●Decomposed inventory replenishment workflows across 17 distribution centers and 400K+ active SKUs by correlating procurement lead times, supplier fill rates, warehouse throughput, and customer demand variability, uncovering optimization opportunities that reduced stockout events by 22%.

●Quantified transportation inefficiencies through lane-level analysis of 180M+ shipment records, integrating carrier performance, fulfillment priorities, route utilization, and delivery SLAs to identify annual cost reduction opportunities exceeding $4.1M.

●Modeled inventory aging and slow-moving product behavior through longitudinal analysis of 36 months of transaction history, enabling proactive inventory disposition strategies and reducing excess inventory exposure by $18M.

PNC Financial Services (Business Analyst) Pittsburgh, PA Feb 2024 - Feb 2025

●Architected enterprise financial analytics platform across Azure Synapse Analytics (Dedicated + Serverless SQL pools), Azure SQL Database, and SQL Server OLTP extracts, designing a hybrid lambda-style architecture separating ingestion, transformation, and consumption layers for banking datasets exceeding 12M+ active customer accounts and 180M+ annual transaction records

●Engineered logical and physical data warehouse design using 3NF-to-dimensional transition modeling, converting normalized core banking schemas (loans, deposits, payments, ledger postings) into analytics-optimized star schemas to support high-performance financial reporting and risk analytics workloads

●Designed multi-domain financial data marts (Retail Banking, Commercial Lending, Treasury, Risk Analytics) with conformed enterprise dimensions (Customer, Account, Product, Branch, Time), enabling cross-functional reconciliation of profitability, liquidity, and credit exposure reporting

●Built enterprise semantic layer abstraction using Power BI dataset models + Synapse views + SQL metadata-driven transformations, decoupling business logic from physical warehouse schema and reducing downstream reporting complexity across 40+ financial reporting assets

●Standardized financial KPI definitions including Net Interest Margin (NIM), Loan-to-Value (LTV), Non-Performing Assets (NPA), Exposure at Default (EAD), and Customer Lifetime Value (CLV) by embedding transformation logic at the semantic layer rather than report level calculations

●Developed complex analytical SQL pipelines using window functions (LAG/LEAD, rolling aggregates), recursive CTEs for account hierarchy traversal, cohort-based time-series segmentation, and conditional aggregation logic across datasets exceeding 120M+ rows of transactional banking data

●Engineered performance-tuned SQL transformations leveraging predicate pushdown optimization, partition pruning strategies, and materialized intermediate staging tables in Synapse dedicated pools, reducing batch processing windows from 6.5 hours to under 3.8 hours

●Designed enterprise-wide data lineage framework mapping data flow from core banking OLTP systems ingestion pipelines Synapse transformation layers Power BI semantic models, enabling full traceability across 900+ regulated financial data elements

●Implemented metadata-driven governance model using business glossary, technical metadata catalogs, and critical data element (CDE) tagging aligned with regulatory compliance standards (audit-ready lineage reconstruction within minutes instead of days)

●Established source-to-report reconciliation framework ensuring regulatory reporting consistency across FR Y-9C, liquidity reports, and internal risk dashboards with full audit traceability

●Developed enterprise-grade Power BI solutions using Import mode + DirectQuery hybrid models, optimizing VertiPaq compression behavior for large financial fact tables and reducing memory footprint across 1.2B+ aggregated rows

●Engineered advanced DAX calculation frameworks using context transition control, filter propagation tuning, calculation groups, and time intelligence optimization (YTD, QoQ, MoM financial comparisons) for enterprise financial dashboards

●Implemented row-level security (RLS) and object-level security (OLS) models integrated with Azure Active Directory, enforcing strict segregation across retail banking, commercial lending, and risk reporting domains

●Performed multi-dimensional risk analytics across loan portfolios using probability-of-default segmentation, delinquency aging curves, and exposure concentration modeling across $4B+ loan book

●Built cohort-based credit behavior analysis models identifying early delinquency patterns, improving risk flagging accuracy by 26% for high-risk customer segments

●Conducted variance analysis across deposits, lending spreads, and profitability drivers, uncovering structural inefficiencies leading to $2.8M in corrected financial exposure adjustments.

Best Buy (Business Analyst) Pune, India Jan 2021 - Jul 2023

●Designed large-scale retail analytics ecosystem integrating AWS S3 data lake, AWS Athena serverless query engine, Tableau, and Power BI semantic models, supporting omnichannel data from e-commerce, in-store POS, inventory systems, and CRM platforms processing 200M+ retail transactions annually

●Built dimensional warehouse models optimized for retail workloads including sales fact tables, inventory snapshots, customer behavior marts, and promotional effectiveness models, enabling multi-channel performance tracking across digital and physical retail ecosystems

●Established conformed retail dimensions (Product, Store, Customer, Channel, Promotion, Time) ensuring consistency across merchandising, marketing, and supply chain analytics systems

●Engineered advanced customer segmentation models using RFM scoring, behavioral clustering, purchase frequency distributions, and recency-weighted transaction modeling across 50M+ customer interactions

●Developed product affinity and basket analysis frameworks leveraging association rule mining principles (market basket analysis logic) to identify cross-category purchase patterns and improve campaign targeting efficiency by 31%

●Built lifecycle analytics models tracking acquisition-to-churn transitions, enabling identification of high-value customer cohorts and improving retention strategy effectiveness by 24%.

●Optimized Athena query performance using partition pruning strategies, columnar Parquet optimization, and predicate pushdown tuning, reducing query runtime by 41% for large-scale sales analytics workloads

●Built reusable SQL transformation layers supporting product hierarchy normalization, sales aggregation logic, and inventory reconciliation across multiple retail systems

●Designed enterprise BI layer using Tableau Hyper extracts and Power BI tabular models, optimizing in-memory aggregation structures for high-performance retail dashboards

●Developed KPI framework covering Revenue, Gross Margin, Inventory Turnover, Conversion Rate, Average Order Value (AOV), Basket Size, and Promotional ROI, standardized across 30+ retail business units

●Enabled self-service analytics adoption by designing semantic-ready datasets reducing dependency on ad-hoc SQL generation by 38%

●Built demand forecasting models leveraging historical sales decomposition, seasonal trend modeling, and promotional elasticity analysis across 10,000+ SKUs

●Conducted multi-dimensional risk analytics identifying seasonal demand shifts across product categories, improving inventory planning accuracy by 27%

●Identified inventory overstock inefficiencies across 10,000+ SKUs, enabling stock rationalization strategies resulting in $3.1M reduction in excess inventory exposure

●Analyzed promotional campaign effectiveness across online and offline channels, improving campaign ROI by 19% through data-driven targeting refinement

EDUCATION

Master of Science in Data Science (University of Houston)

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