Neeraj Kumar | Data Engineer & BI Developer

About Me

I'm a Data Engineer & BI Developer based in Bengaluru, India, currently at Basal Analytics — where I build end-to-end data solutions covering ingestion, transformation, and loading, designed to power scalable and high-performance BI systems for enterprise clients.

I design and implement robust ETL/ELT pipelines, leverage medallion architecture (Bronze/Silver/Gold), and deliver production-grade dashboards on Power BI, Tableau & Looker Studio. I've worked with clients including Aligned Global Marketing, Equipment Corporation of America, ND Global Consultancy, and Queen of the Thrones.

My stack spans Google Cloud Platform (GCS, BigQuery, Cloud Functions), Microsoft Fabric, Azure, SQL & Python — with a strong focus on data quality checks, lineage tracking, and automated CI/CD pipelines. I've also built CI/CD-driven integrations across platforms like Shopify, ShipBob, MongoDB, TikTok & ShipStation for seamless, scalable data workflows.

0+ Years Experience

0+ Projects Delivered

0+ Enterprise Clients

0 Active Clients

Skills & Tools

🐍

Programming & Analytics

Python SQL NumPy Seaborn EDA Google Colab

⚙️

Data Engineering

ETL / ELT Microsoft Fabric BigQuery Snowflake SQL Server MongoDB Medallion Architecture

📊

BI & Visualisation

Power BI Tableau Looker Studio DAX Excel

☁️

Cloud & DevOps

GCP Azure Cloud Run Cloud Scheduler CI/CD GitHub Actions

Experience

Data Engineer & BI Developer

Basal Analytics

Oct 2025 – Present

Engineered a metadata-driven incremental ETL pipeline in Azure Data Factory (on-prem SQL Server → Azure SQL MI, 1,300+ tables) for ECA, replacing per-table looping with set-based SQL and row-hash change detection — enabling true incremental sync without native CDC.
Engineered and managed end-to-end data pipelines for ND Global Consultancy, enabling seamless ingestion of JSON-based data from MongoDB into Microsoft Fabric. Designed and implemented a scalable Medallion Architecture (Bronze, Silver, Gold) using Fabric Notebooks (Spark-Python) and T-SQL, ensuring structured data transformation, strong data lineage, and enterprise-grade analytics readiness across the organization.
Designed and developed data pipelines within Microsoft Fabric Data Factory using multiple ingestion approaches, including data mirroring and custom ingestion frameworks built in Fabric Notebooks using Python. Implemented multi-stage transformation workflows and optimized pipeline execution to prevent capacity overload, minimize failures, and ensure consistent, reliable data processing at scale.
Built and optimized curated Gold-layer flat tables using T-SQL to support high-performance Power BI dashboards. Focused on scalable schema design, standardized KPI definitions, and clean, business-ready datasets, enabling accurate reporting and faster decision-making for stakeholders across functions.
Worked extensively across multi-platform data ecosystems, including SSMS, GCP, GCS, BigQuery, and Microsoft Fabric. Improved data quality, pipeline reliability, and performance through optimization techniques, reducing latency and enhancing the efficiency of end-to-end data workflows.
Developed and maintained data ingestion frameworks on Google Cloud Platform (GCP), integrating multiple external data sources such as Shopify, ShipBob, ShipStation, TikTok, and Amazon through CI/CD-enabled pipelines. Built automated workflows using Cloud Functions and Google Scheduler to ingest and store data in Google Cloud Storage (GCS) on a daily basis.
Designed downstream data processing pipelines to transfer and load data from GCS into BigQuery using parquet-based structured storage. Implemented transformation layers using Dataform, organizing datasets into Silver and Gold layers to support analytics use cases. Ensured automated, reliable daily execution of pipelines, maintaining data consistency and availability for reporting and BI applications.
Collaborated closely with cross-functional stakeholders — including business, analytics, and engineering teams — to align data engineering solutions with reporting requirements and organizational goals. Contributed to building scalable, future-ready data infrastructure that supports continuous growth and data-driven decision-making.

Microsoft FabricMongoDB Medallion ArchitectureT-SQL BigQueryGCP DataformCloud Functions Power BICI/CD Git Integration

Executive

IIT Kanpur Development Foundation

Feb 2025 – Sep 2025

Improved operational efficiency by ~20% via alumni prospecting dataset optimisation.
Built Power BI & Looker Studio dashboards for stakeholder decision-making.
Designed end-to-end alumni donation pipeline enabling targeted donor outreach.
Cleansed & enriched Zoho CRM data, improving downstream analytics reliability.

Power BILooker Studio Zoho CRMPython

Data Analyst Intern

Mensa Brands

Sep 2024 – Feb 2025

Led end-to-end inventory allocation operations for beauty, personal care, and fashion brands across domestic online B2B channels, improving supply chain efficiency by 7% through DRR-based planning, demand forecasting, and cross-functional coordination.
Analyzed sales trends and purchase order data to drive data-backed inventory decisions, optimizing stock levels to align with demand while reducing overstocking and stock imbalances across categories.
Executed PO fulfillment and replenishment planning for key platforms including Amazon, Myntra, Flipkart, Zepto, Blinkit, and other quick commerce partners, ensuring timely dispatches and consistent product availability.
Monitored inventory across e-commerce platforms and strategically allocated stock to minimize overstocking at partner warehouses while improving overall inventory efficiency.
Coordinated inter-warehouse stock transfers to address inventory shortages, balance stock across locations, and sustain a fulfillment rate above 90% through uninterrupted order processing.

SQLExcel Supply Chain Analytics

Analyst (Part Time)

Puma

Dec 2019 – Jan 2022

Led daily store operations and tracked KPIs (sales, ATV, conversion), enabling the team to consistently achieve 100% of sales targets through strategic customer engagement and seasonally aligned merchandising.
Managed inventory by analyzing fast- and slow-moving products, ensuring high-demand availability while reducing stockouts during peak periods.
Built partnerships with gym managers and onboarded them to Puma's discount programs, driving a 5% increase in performance-category sales and expanding brand reach.
Streamlined inventory by coordinating with the audit team to remove off-season, aged, and non-moving stock, improving turnover and maintaining a lean, sale-ready assortment.

KPI AnalysisSales Analytics

Projects

🎬

Netflix Content Strategy — Business Case EDA

Performed end-to-end EDA on 8,807 Netflix titles across 749 countries to uncover content trends, rating distributions, and geographic gaps. Delivered strategic recommendations covering family content expansion, talent collaboration, and growth markets in Southeast Asia, Africa & Latin America.

Python EDA Pandas Seaborn Business Analysis

☁️

Shopify → GCS Automated Ingestion Pipeline (CI/CD)

Production-grade data pipeline that extracts orders, products, customers, inventory & more from the Shopify Admin REST API and lands them as Hive-partitioned Parquet files in GCS (Bronze layer). Runs as a scheduled Cloud Run Job — built and redeployed automatically on every push via GitHub Actions CI/CD. Supports both daily incremental loads and full historical backfills with cursor-based pagination and rate-limit handling.

Python GCP Cloud Run GitHub Actions Shopify API Parquet CI/CD