Mohit G

Power BI Analytics & Data Integration Platform

Mar 2026 - Jun 2026 • 3 mos

Built enterprise Power BI analytics solutions integrating REST APIs, SQL databases, CRM, finance, property management, and AI call analytics systems. Designed star-schema dimensional models and governed semantic layers with reusable KPIs and advanced DAX calculations including time intelligence and relationship handling. Optimized performance through model tuning and DAX refactoring, implemented scheduled and incremental refresh, and delivered executive dashboards with drill-through insights, reconciliation frameworks, and reusable PBIT templates. Tools & Technologies Power BI, DAX, SQL, REST APIs, JSON, Power Query, CRM Systems, Property Management Systems, Financial Systems Business Impact Unified reporting across multiple disconnected enterprise systems Improved KPI consistency and governance through centralized semantic models Increased dashboard performance and scalability Delivered near real-time operational and financial insights Enabled reusable reporting frameworks for faster client onboarding and deployment Capability Demonstrated Expertise in enterprise Power BI development, dimensional modeling, advanced DAX engineering, multi-source integration, semantic layer design, performance optimization, executive dashboard design, and scalable analytics solution delivery.

Data Modelling - CPG Marketing, Sales & Promotion Data Modeling Platform

Dec 2025 - Mar 2026 • 3 mos

Designed and enhanced CPG marketing, sales, and promotion data models supporting multiple brands, markets, and POS platforms. Led business requirement gathering, data profiling, conceptual/logical/physical modeling, STTM creation, and DDL design. Built scalable ETL-ready models with SCD concepts and harmonized new market data. Supported reverse engineering of legacy models and governed delivery processes enabling scalable BI and analytics solutions. Tools & Technologies Erwin, SQL, Power BI, ETL Pipelines, Data Warehousing, STTM, Dimensional Modeling Business Impact Established scalable and governed CPG analytics models Improved consistency across marketing, sales, and promotion reporting Accelerated ETL delivery through structured STTM and DDL design Enabled faster onboarding of new markets and datasets Improved collaboration between business, modeling, and engineering teams Capability Demonstrated Expertise in enterprise data architecture, CPG domain modeling, dimensional design, lineage management, and scalable analytics foundation development.

Microsoft Fabric Migration using Medallion Architecture

Jun 2025 - Dec 2025 • 6 mos

Designed and implemented an enterprise Microsoft Fabric platform by migrating Azure SQL DBs, ADF pipelines, notebooks, flat files, and Power BI workloads. Built a medallion architecture with Bronze, Silver, and Gold layers using Lakehouse and Warehouse models. Implemented ELT processing, SCD2, incremental loads, Direct Lake reporting, governance, RLS, and monitoring frameworks. Optimized performance using partitioning, V-Order, shortcuts, and parallel API processing to deliver scalable, secure, and cost-efficient analytics. Strategic Approach & Solution Designed and implemented a modern Microsoft Fabric Medallion Architecture for scalable and governed analytics. 1. Medallion Architecture Implemented layered architecture: Landing Zone for raw ingestion from Workday APIs, Salesforce, ADLS, and flat files Bronze Layer (Lakehouse) for replica/staging data Silver Layer (Lakehouse) for cleansing, harmonization, normalization, and common data model creation Gold Layer (Warehouse) for semantic and dimensional models optimized for BI workloads and concurrent users 2. Migration & Modernization Migrated: Azure SQL DB workloads ADF pipelines Notebooks Flat file integrations Power BI reports and datasets Modernized: 14 datamarts 35+ Power BI reports 80 datasets 3. ELT & Incremental Framework Implemented ELT processing using Fabric pipelines and notebooks Built incremental loading across Bronze, Silver, and Gold layers Implemented: SCD Type 2 history tracking Parameterized pipelines and notebooks Metadata-driven orchestration 4. Performance & Cost Optimization Optimized processing and compute usage using: Parallel API extraction using RDD partitioning Vacuuming Lakehouse tables to reduce storage volume V-Order optimization for faster reads Partitioning and Z-Order on high-filter columns Lightweight Python execution for small datasets to avoid unnecessary Spark spin-up and billing overhead