ARVIND KUMAR

Senior Data Modeler with 15+ years of experience, including 4+ years specializing in dimensional data modeling and enterprise data architecture within banking and financial domains. Recently expanded into Azure Data Engineering with hands-on experience building end-to-end data pipelines using Azure Data Factory, ADLS Gen2, and Databricks.

Expertise in designing scalable Star and Snowflake schemas, implementing SCD strategies, and optimizing data models for high-performance analytics. Strong ability to bridge business requirements with modern cloud data platforms.

Azure Data Engineering Project – Trade Data Pipeline

• Designed and implemented an end-to-end data pipeline using ADF, ADLS, Databricks (PySpark), Synapse Analytics, and Power BI.
• Performed source analysis, identified business entities, defined fact table grain, and designed a star schema (Fact_Trade, Dim_Account, Dim_Product, Dim_Customer, Dim_Date).
• Implemented physical data model in Azure Synapse with optimized distribution and indexing strategies.
• Ingested data into ADLS Bronze layer in raw format (CSV/TXT) for auditability and traceability.
• Transformed data using Azure Databricks (PySpark) and stored in Parquet format in Silver layer for improved performance and compression.
• Implemented incremental loading using watermark logic for Fact_Trade.
• Applied SCD Type 2 for dimensional tables (Account, Product, Customer) to maintain historical data.
• Developed surrogate key generation and lookup logic to ensure referential integrity.
• Built Gold layer in Synapse Analytics for curated, analytics-ready data.
• Loaded Dim_Date as a static dimension in Gold layer.
• Created interactive dashboards in Power BI for business reporting.
• Orchestrated end-to-end pipeline using Azure Data Factory, including scheduling and monitoring.

ADF → ADLS (Bronze – Raw) → Databricks (Silver – Parquet) → Synapse (Gold) → Power BI

• Banking & Financial Services
• Insurance
• ERP Systems
• Logistics