1. Banking Domain

Project 1: Real-Time Fraud Detection Pipeline

I developed a real-time fraud detection system for a major retail bank using Apache Kafka, Apache Flink, and Python.

I ingested real-time transaction streams from multiple sources, including credit card swipes, mobile apps, and ATMs, into Kafka topics, and processed them using Flink streaming jobs.

I created a sliding window-based analysis to detect anomalies such as location mismatches, high-frequency transactions, and blacklisted merchants.

I used Python with Flink SQL and CEP (Complex Event Processing) patterns to flag suspicious transactions.

I integrated Redis to store frequently accessed metadata and Apache HBase for historical patterns.

I also built dynamic risk scoring models that relied on machine learning outputs sourced from Databricks MLflow models.

To complete the project, I orchestrated the pipeline using Apache Airflow and built dashboards in Tableau for fraud analytics.

I used AWS S3 for raw transaction archival, and Amazon RDS for metadata storage. This end-to-end system helped reduce manual fraud investigations by 40%.

Project 2: Customer 360 View for Personalized Banking

I worked on a Customer 360 solution for a global bank aiming to create a unified view of customer data spread across 10+ legacy systems.

I used Apache NiFi to ingest structured and unstructured data from Oracle DB, SQL Server, MongoDB, and flat files into a centralized Hadoop HDFS lake.

I developed PySpark ETL jobs to clean, standardize, and merge customer data into a golden record, resolving conflicts using rules around recency and data confidence scores.

The curated data was stored in a Hive data warehouse and exposed via Presto for analysts.

I implemented Delta Lake to manage slowly changing dimensions and ensure ACID compliance.

To enable personalized insights, I integrated the golden data set into Power BI and enabled marketing to tailor campaigns based on customer segments.

I used Azure DevOps for CI/CD pipelines and GitHub for version control, ensuring collaborative development across teams.

Project 3: Loan Origination & Underwriting Automation

I developed a scalable loan origination data pipeline for a national bank looking to modernize its underwriting process.

Data sources included customer application forms, credit bureau APIs (Equifax, TransUnion), and internal banking history stored in Oracle and MongoDB.

I used Apache NiFi and Kafka to create ingestion pipelines that fed into a centralized AWS S3 data lake.

Using AWS Glue and PySpark, I built ETL jobs to validate income, extract document metadata (from PDFs using Textract), and normalize credit score parameters.

I developed rules using Python for eligibility checks (like debt-to-income ratio, employment stability, and credit utilization) and persisted qualified applications in Amazon Redshift for dashboarding.

For real-time decisioning, I integrated this data pipeline with a rules engine built in Drools, and exposed it via a RESTful API consumed by the front-end loan officers’ portal.

This automation reduced loan processing time by 50% and increased throughput without compromising compliance.

Telecom Domain

Project 1: Network Usage Analytics on Big Data Stack

I worked on a big data analytics solution for a telecom giant to analyze terabytes of CDR (Call Detail Records) daily. I used Apache Sqoop and Kafka to ingest structured and semi-structured data into HDFS and Hive tables.

I developed PySpark jobs to analyze call durations, dropped calls, roaming patterns, and bandwidth consumption.

I also used Apache HBase to store real-time metrics and Apache Druid for fast OLAP queries. Data scientists used this clean, transformed data to run churn prediction models.

I exposed the processed data through Superset dashboards and APIs built in Flask for internal telecom ops teams.

We achieved near real-time network monitoring using Airflow DAGs for hourly updates and alerts for anomalies using Grafana and Prometheus.

Project 2: 5G Rollout and User Location Data Platform

I developed a platform to collect and process geo-location data for 5G coverage optimization. We collected device signal data via mobile SDKs and sent them through Kafka topics for ingestion.

I used Apache Beam with Google Cloud Dataflow to process these signals and aggregated data per tower, per region.

I implemented BigQuery for OLAP analysis and used GeoJSON with PostGIS to map user density in real time.

I used Airflow to orchestrate daily heatmap generation and Looker for dashboard visualization.

The processed output enabled the 5G planning team to optimize antenna placement and reduce dead zones by 23%.

I worked closely with the DevOps team to containerize the jobs using Docker and deployed them using Kubernetes (GKE).

Project 3: Telecom Billing Pipeline Modernization

I modernized an old telecom billing system by reengineering it using a distributed data engineering stack.

I migrated ETL processes from PL/SQL to Spark Structured Streaming to handle streaming events from customer calls, SMS, and data usage.

I used Apache Kafka for ingesting CDR events, Spark for real-time enrichment using reference data (customer plans, taxes), and Delta Lake to maintain billing state with full audit logs.

I wrote transformation logic in Scala and orchestrated batch jobs using Airflow.

Processed bills were stored in Snowflake, and I built custom validation layers using dbt for downstream BI reports.

This pipeline reduced latency by 60% and ensured accurate billing for over 10 million customers.

Health Domain

Project 1: Patient Data Lake for Health Analytics

I designed a centralized patient data lake for a health insurance provider using AWS S3, Glue, and Athena. I ingested clinical, claims, and wearable data using AWS DMS, Kafka Connect, and Lambda functions.

I developed PySpark ETL pipelines on AWS Glue to cleanse and join data sets into a unified patient profile.

I used Delta Lake to manage schema evolution and versioning, ensuring traceability.

I also implemented de-identification logic using Python to adhere to HIPAA compliance.

To support analytics, I created Athena views and dashboards in QuickSight, allowing actuaries to analyze patient cohorts and claim patterns. The platform improved care personalization and reduced claim fraud.

Project 2: Claims Denial Analytics & Prediction Platform

I worked on building a data-driven solution to analyze and predict healthcare claims denials for a large insurance provider.

The core goal was to reduce the rate of denied claims, which cost millions annually.

I ingested EDI 837 claim files, enrollment data, and provider details into Azure Data Lake using Azure Data Factory and Kafka for near real-time integration.

I developed ETL pipelines in Azure Databricks using PySpark to clean, transform, and normalize data.

I built features such as CPT code combinations, diagnosis match rates, provider error history, and claim aging.

These were used to train an ML model in Azure Machine Learning Studio that could predict potential denials before submission 

The analytics layer was built on Power BI to help internal claim processors drill down by provider, location, or denial reason.

I also implemented Azure Monitor and Log Analytics for tracking ETL job health and data anomalies.

This platform led to a 27% drop in denial rates and improved processing efficiency by 35%.

Project 3: EHR Data Integration Using FHIR

I integrated multiple Electronic Health Record (EHR) systems using the FHIR standard.

I created data pipelines that ingested HL7 and JSON FHIR resources using Apache NiFi and Kafka.

I used Python scripts to validate schema, handle nested fields, and persist structured data into PostgreSQL and MongoDB depending on query patterns.

The data was further transformed and loaded into Redshift for analytics.

I enabled physicians to access consolidated patient histories through dashboards, reducing data retrieval time by 70%.

I also implemented audit logging and token-based access controls using OAuth2.0 to secure access.

Retail Domain

Project 1: Omnichannel Sales Data Lake

I developed an omnichannel retail data lake for a leading global retailer to consolidate data from in-store purchases, e-commerce platforms, mobile apps, and third-party vendors.

I used AWS Glue and S3 to ingest and store raw transactional data from systems like Salesforce, Shopify, and SAP.

I built PySpark ETL jobs that cleansed and enriched product, inventory, and sales data.

I handled deduplication and currency normalization across regions and wrote business logic to compute KPIs like sales per SKU, stock-to-sales ratios, and seasonal trends.

I stored the cleaned data in Amazon Redshift and applied dbt for semantic modeling and metrics standardization.

The team used Looker and Tableau for visual analytics. This project enabled stakeholders to get a single source of truth for product performance, allowing for 20% better inventory distribution and a 30% improvement in forecasting accuracy.

Project 2: Customer Behavior & Recommendation Engine

I worked on a customer behavior analytics platform using Databricks, Delta Lake, and Azure Data Factory to gather insights into customer preferences.

Data from loyalty programs, web logs, and mobile app activity was streamed via Kafka and processed using Spark Streaming.

I created sessionization logic using window functions in PySpark and applied collaborative filtering techniques for recommendations.

I integrated MLflow for model tracking and Azure Synapse Analytics for aggregating results by demographic segments.

I also used Azure Key Vault to manage access securely.

The output of these analytics fed into our recommendation engine that boosted cross-selling and up-selling conversions by 18%.

The marketing team was able to launch personalized campaigns directly based on the user clusters we identified.

Project 3: Supply Chain Optimization and Stock Movement

I built a real-time inventory tracking and supply chain pipeline for a chain of 1,500 stores across North America.

I ingested RFID, barcode scans, and logistics updates via Apache Kafka and stored them in HDFS using Apache Hudi for incremental updates and time-travel capabilities.

I used Spark Structured Streaming to correlate warehouse dispatches with store deliveries and real-time shelf replenishment data.

I enriched this with product master data from SAP BW and weather forecasts to anticipate logistics delays.

The output of this pipeline was visualized in Grafana and also exposed as APIs for real-time mobile inventory lookup. This drastically reduced stockouts and improved warehouse-to-shelf time by 25%.

5. Payroll Domain

Project 1: Payroll Processing & Tax Compliance Engine

I worked on a payroll engine for a multinational enterprise that processed payments for over 80,000 employees across 10 countries.

The raw input data included employee time logs, benefits info, and tax rules, which I ingested using Apache NiFi and stored in Amazon S3.

I wrote ETL logic in Scala using Apache Spark to process time sheets, calculate gross and net pay, deduct taxes (including TDS, PF, 401k, etc.), and apply bonuses and leave encashments.

Tax brackets were managed via DynamoDB, and results were stored in PostgreSQL and Snowflake for compliance auditing.

The processed results were integrated with SAP SuccessFactors and reported using Power BI.

My automation eliminated manual intervention and cut payroll cycle time from 3 days to 6 hours while ensuring statutory compliance.

Project 2: Pay Stub Generation and Archival Platform

I developed a scalable system to generate and store over 1 million digital pay stubs monthly for a leading payroll processing firm.

I ingested structured and semi-structured employee data using Azure Data Factory and stored it in Azure Data Lake Gen2.

Using PySpark on Azure Databricks, I implemented transformation logic and created templates for PDF generation using Apache PDFBox.

I orchestrated the entire pipeline using Azure Data Factory pipelines and triggered alerts for failures using Azure Monitor.

Generated pay stubs were encrypted and archived in Azure Blob Storage, and access was provided through a secure React-based web portal with OAuth2.0 authentication.

This reduced operational burden and also enhanced employee satisfaction through easy access to historical payslips.

Project 3: Salary Forecasting & Workforce Analytics

I led the development of a predictive analytics engine for salary forecasting and workforce trend analysis.

I ingested HRMS data from Workday, employee profiles, and project allocations into a GCP BigQuery warehouse.

Using Python with Scikit-learn, I developed salary forecasting models based on tenure, skills, past appraisals, and market trends.

I scheduled model training and batch scoring using Vertex AI Pipelines and integrated model predictions with Looker dashboards.

This tool helped HR teams anticipate payroll budget hikes and identify attrition-prone employee segments.

The forecasts had a 91% accuracy rate, helping to streamline resource planning and financial forecasting.

6. Finance Domain

Project 1: Credit Risk Data Platform

I built a data platform for a finance company to assess credit risk across personal loans, credit cards, and mortgages.

I ingested transactional data, credit bureau scores, and behavioral signals using Kafka Connect, AWS Lambda, and S3.

Using EMR Spark clusters, I wrote ETL jobs to merge borrower data, apply risk rules (like DTI ratios, late payment history), and prepare features for model training.

The risk scoring models were developed in SageMaker and predictions were pushed back into Redshift for access by underwriting systems.

I also created lineage tracking using AWS Glue Data Catalog and access control using Lake Formation.

This platform enabled real-time risk analysis and significantly reduced loan default rates.

Project 2: Investment Portfolio Aggregation Platform

I developed a data pipeline to unify investment portfolios of customers spread across brokerage accounts, mutual funds, 401(k), and crypto wallets.

I used Apache Airflow to orchestrate daily batch jobs fetching data from APIs like Plaid, Alpaca, and custodians like Fidelity.

I used Pandas and PySpark to normalize asset types, calculate returns, risk-adjusted metrics (Sharpe, Alpha), and fees.

All processed data was stored in Google BigQuery for interactive queries and portfolio rebalancing analysis.

I designed interactive dashboards in Tableau where wealth advisors could track their clients’ net worth, diversification, and risk profile.

The platform drove personalized advisory and added a new revenue channel for the firm.

Project 3: Financial Forecasting & Budgeting System

I worked on a budgeting platform that supported financial planning for a global manufacturing company.

I ingested ERP, CRM, and historical financial data into Snowflake using Fivetran and Kafka.

I designed and developed ETL logic in dbt to calculate OPEX, CAPEX, cash flow trends, and revenue forecasts.

I integrated Prophet for time-series forecasting and used Streamlit to build internal tools for finance teams to adjust assumptions and simulate scenarios.

This solution gave CFOs real-time control over budgeting cycles and enabled them to cut planning cycles by over 40%.

It also empowered teams to plan based on actuals, seasonal adjustments, and market variables.