Data Command Center / Portfolio
Aarya Mody
Data Engineer & Data Analyst
I design governed lakehouse pipelines, analytics models and decision systems across Azure Databricks, SQL, Python and Power BI.
Proof Console
Evidence for reliable pipelines, governed models and business-ready analytics.
Each signal maps to systems I have worked on: lakehouse migration, quality validation, forecasting, reporting performance and operational automation.
Lakehouse migration
Azure Databricks Bronze, Silver and Gold architecture for production reporting modernization.
Direct Lake foundationQuality governance
Schema, relationship, audit and semantic validation across five operational data domains.
Five governed domainsAutomation impact
Python and SQL workflows that reclaimed manual reporting time for cross-functional teams.
20+ hours saved weeklyForecasting accuracy
Demand workflow supporting budget and inventory planning with measurable model accuracy.
<5% MAPESystems Architecture
From raw operational data to trusted leadership decisions.
A compact view of the data path I work across: ingestion, transformation, validation, semantic modeling and analytics delivery.
Bronze
Source Capture
ERP / CRM / Operational streams
Silver
Validated Models
PySpark, SQL, schema checks
Gold
Decision Layer
Power BI, DAX, KPI logic
Case Study Explorer
Five project narratives with problem, system, tools and result.
Data Engineering
Medallion Architecture Migration
Enterprise migration from legacy production reporting to Azure Databricks Bronze, Silver and Gold layers for governed Direct Lake analytics.
- Problem
- Fragmented production reporting needed a governed lakehouse foundation.
- System
- Validated layers, schemas, transformations and auditability in Azure Databricks.
- Tools
- Databricks, Delta Lake, PySpark, SQL, Power BI Direct Lake.
- Result
- Scalable analytics foundation for four global business units.
Data Quality
Semantic and Pipeline QA Governance
Validation across Inventory, Quality, Planning, Purchases and Production domains before analytics release.
- Problem
- Vendor-delivered models needed trusted definitions and transformation evidence.
- System
- Structured QA cycles for source-to-target mappings, DAX logic and remediation logs.
- Tools
- SQL, Power BI, DAX, QA logs, schema validation.
- Result
- Five governed domains and 33 performance-tuned metric equivalents.
Analytics
Demand Forecasting Pipeline
Python workflow for planning-ready demand signals and budget-aware inventory decisions.
- Problem
- Planning teams needed reliable forward-looking demand signals.
- System
- Cleaned data, modeled demand and validated accuracy before reporting.
- Tools
- Python, Pandas, statistical modeling, SQL, reporting outputs.
- Result
- Below 5% MAPE and EUR 500K+ in planning decisions supported.
SQL + ETL
Data Quality and Performance System
Reporting pipeline cleanup across ERP and CRM sources through tuning and shared staging logic.
- Problem
- Slow SQL jobs and disconnected sources delayed analytics refreshes.
- System
- Query plan review, stored procedure cleanup and consolidated ingestion logic.
- Tools
- SQL, stored procedures, ERP / CRM staging, ETL review.
- Result
- 40% faster SQL pipelines and 30% higher data consistency.
Automation
Smartsheet Synchronization Framework
Cross-department synchronization across three workspaces while preserving hierarchy and ownership.
- Problem
- Teams needed shared status visibility without losing workspace ownership.
- System
- Designed sync logic, hierarchy preservation and tracking views.
- Tools
- Smartsheet workflows, automation logic, operational reporting.
- Result
- Cleaner project visibility and less manual status consolidation.
Experience Timeline
Hands-on delivery across analytics modernization, pipeline quality and reporting systems.
Data Analyst | Analytics & Digital Intelligence
Brentwood Industries, Inc.
- Lead Medallion Architecture migration to Azure Databricks Bronze, Silver and Gold layers.
- Validate Silver-to-Gold ETL transformations, table schemas, audit trails and governance controls.
- Map 16 legacy metrics to 33 performance-tuned equivalents for Direct Lake analytics.
- Own QA governance across Inventory, Quality, Planning, Purchases and Production.
Azure Databricks
PySpark
SQL
Delta Lake
Power BI
Data Analyst (Data Pipelines & Analytics)
IITS - Integrated IT Solutions
- Automated five reporting workflows with Python scripts and SQL stored procedures.
- Improved SQL reporting pipeline performance by 40%.
- Unified five ERP and CRM sources into one staging pipeline.
- Delivered 10+ Power BI dashboards adopted daily by 50+ stakeholders.
Python
SQL
Power BI
Forecasting
ERP / CRM
Data Analyst Intern
IITS - Integrated IT Solutions
- Authored production SQL queries and Python cleaning scripts for analytical environments.
- Documented data models, source-to-target mappings and business transformations.
- Prototyped relational data models and reporting assets that moved into production use.
Skill Architecture
Technical range organized around delivery outcomes.
Data Engineering
Azure Databricks, Delta Lake, Medallion Architecture, PySpark, ETL / ELT, Direct Lake
Analytics
SQL, Python, Pandas, forecasting, KPI design, source analysis, requirements mapping
Business Intelligence
Power BI, DAX, Power Query, semantic models, dashboards, drill-through reporting
Governance
Schema validation, source-to-target mapping, audit trails, data quality controls, QA cycles
Automation
Python scripting, SQL stored procedures, Smartsheet workflows, recurring report automation
Credentials
Education, certifications and communication range.
Bachelor of Science - Information Technology
Parul University | 2020 - 2023 | GPA: 8.73/10
Relevant Credentials
Microsoft Power BI Data Analyst Associate (PL-300), SQL for Data Analysis, Deloitte Australia Data Analytics Job Simulation, Learning Excel: Data Analysis.
Communication Range
English C2 Proficient, German A2 and pursuing B2, Hindi and Gujarati native.
Google Developer Student Club Lead
Organized a 100+ participant technology hackathon and led student developer initiatives.
Contact Node
Ready for data engineering and analytics conversations.
For recruiter screens, data roles or analytics projects, email and LinkedIn are the fastest ways to reach me.