Portfolio - Bickram Mondal | Codebasics

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Bickram Mondal

Data Aspirant

EMAIL

[email protected]

PHONE

+91 9674587448

Download CV

About Me

I am a Data Engineer with 4+ years of experience in ETL development, data migration, and enterprise data processing solutions. At Wipro, I have designed, optimized, and supported production-grade ETL workflows using Informatica PowerCenter, SQL, Syniti, and SAP HANA, helping improve data quality, operational reporting, and migration efficiency across large-scale business systems.

Alongside my professional experience, I completed an M.Tech in Software Systems from BITS Pilani while working full-time, strengthening my foundation in scalable software systems and data engineering practices. I am also a Databricks Certified Professional Data Engineer and Databricks Certified Associate Data Engineer, validating my knowledge of modern data platforms, distributed processing, and Lakehouse architectures.

To expand my expertise in cloud and big data technologies, I have built hands-on data engineering projects using Databricks, Apache Spark, PySpark, Delta Lake, AWS S3, Athena, Lambda, and SSR

2

Databricks Projects

1

PowerBI Project

Key Skills

Databricks

Apache Spark

SQL

AWS Glue

Python

PySpark

AWS S3

Delta Lake

Lakeflow Declarative Pipeline

Snowflake

Apache Airflow

My Projects

Awards & Certificate

Databricks Associate Data Engineer - Databricks Academy

Databricks Associate Data Engineer - Databricks Academy

Databricks Professional Data Engineer - Databricks Academy

Databricks Professional Data Engineer - Databricks Academy

Let's Work Together!

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

EMAIL

[email protected]

Call Me

+91 9674587448

LinkedIn

https://www.linkedin.com/in/bickram-mondal-1567friday/

GitHub

https://github.com/Bickram-Mondal

Databricks Project

Company Acquisition Data Integration Using Spark Distributed Framework

Domain/Function: FMCG Domain

Built a Databricks-based data integration solution for an FMCG acquisition scenario, aligning acquired company data with the parent organization's enterprise model. Used PySpark, Delta Lake, and Medallion Architecture to standardize and transform data into analytics-ready datasets.

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Engagement

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on GitHUB

Company Acquisition Data Integration Using Spark Distributed Framework

Engineered a Gold-layer fact table in Databricks by creating hash-based surrogate Product Keys, standardizing acquired company data, and enabling consistent joins, reporting, and downstream analytics.

Company Acquisition Data Integration Using Spark Distributed Framework

Engineered a scalable Databricks pipeline to resolve schema mismatches in acquired FMCG data through Bronze, Silver, and Gold transformations, producing unified datasets for enterprise-wide reporting and insights.

Project Details

This project simulates an FMCG acquisition scenario where a parent company acquires a smaller organization whose data structure does not align with the enterprise data model. The acquired company's datasets contained schema mismatches, missing business keys, inconsistent attributes, and non-standardized formats, making them unsuitable for unified reporting and analytics.

To address this challenge, I built a Databricks-based data integration solution using Medallion Architecture, PySpark, Delta Lake, SQL, and AWS S3. Raw datasets were ingested into the Bronze layer, transformed and standardized in the Silver layer through business-rule-driven mappings, key generation, schema alignment, and data quality validations, and then curated in the Gold layer to match the parent company's reporting standards. The final datasets were integrated into an analytics-ready model, enabling consistent enterprise reporting, improved data quality, and faster access to business insights.

Databricks Project

Company Acquisition Data Integration Using Spark Distributed Framework

Domain/Function: Sales Analytics

Solved the challenge of analyzing large-scale cab service data across multiple cities by building a Databricks Lakehouse pipeline that delivers trusted datasets for reporting and business insights.

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Engagement

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on GitHUB

Other Tools

AWS S3

Lakeflow Declarative Pipeline

Genie (AI available in Databricks)

Company Acquisition Data Integration Using Spark Distributed Framework

Designed a Databricks Lakehouse pipeline that ingests daily cab service data from AWS S3, processes it through Bronze, Silver, and Gold layers using Lakeflow and Delta Lake, and delivers city-wise analytics-ready datasets for BI reporting.

Company Acquisition Data Integration Using Spark Distributed Framework

Databricks Lakeflow pipeline lineage showing incremental trip data processing through staging, dimension, and fact tables. Curated fact datasets are then materialized into city-specific analytical views, enabling scalable reporting and insights.

Project Details

This project demonstrates the design of a scalable cab service analytics platform using Databricks Lakehouse Architecture to process trip and operational data collected across multiple Indian cities. Daily data files are ingested into AWS S3 and incrementally processed using Lakeflow Declarative Pipelines, enabling efficient handling of continuously growing datasets. Following the Medallion Architecture pattern, raw data is stored in the Bronze layer, cleansed and transformed in the Silver layer, and aggregated into business-ready Gold tables using PySpark, SQL, and Delta Lake. City-specific materialized views are then generated to support analytical reporting on ride demand, revenue trends, weekday versus weekend performance, and holiday-based travel patterns. The solution delivers reliable, analytics-ready datasets that help stakeholders identify regional trends, improve operational planning, and support data-driven business decisions.

PowerBI Project

Sales Analytics With PBI Desktop

Domain/Function: Sales

Built an interactive Sales Analytics Dashboard in Power BI using data modeling, DAX, and visual analytics to track sales performance, customer trends, product insights, and freight costs across multiple countries with dynamic filtering capabilities.

View Project
Engagement

View Project
on GitHUB

Other Tools

Power BI Desktop

Power Query

DAX

Data Modeling

Data Visualization

Sales Analytics With PBI Desktop

Built an interactive Power BI Sales Analytics Dashboard featuring KPIs, category-wise sales, customer performance, product contribution, and freight analysis with dynamic filters for Country, Month, Quarter, and Year.

Sales Analytics With PBI Desktop

Designed a relational Power BI data model connecting Customers, Orders, Order Details, Products, and Categories tables, with an aggregated Freight table to enable accurate sales analysis, KPI calculations, and interactive reporting.

Project Details

Developed an end-to-end Sales Analytics Dashboard in Power BI using a relational data model built from Customers, Orders, Order Details, Products, Categories, and a custom Freight by Ship Country table. Performed data modeling by establishing relationships between fact and dimension tables, creating calculated columns and measures to derive key business metrics such as Net Sales, Total Orders, Product Count, Customer Count, and Freight Cost. Designed an interactive dashboard with slicers for Country, Month, Quarter, and Year, enabling dynamic analysis across multiple dimensions. Built visualizations to analyze sales performance by customer, category, product, and shipping country, helping uncover top-performing products, customer trends, and freight distribution patterns. The solution demonstrates data modeling, DAX calculations, KPI reporting, and interactive business intelligence capabilities in Power BI.