Highly experienced Data Engineer with 4 years of expertise in designing, developing, and optimizing robust data solutions across diverse industries including Retail, Healthcare, Automotive, and Energy. Proven ability to implement scalable ETL/ELT pipelines, leverage advanced cloud technologies (Azure, Databricks, Fabric), and deliver actionable insights through analytical projects. Committed to driving business value by transforming complex data into strategic assets.
Data Engineer
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Summary
As a Data Engineer at Cognizant, I lead end-to-end data engineering initiatives, designing and implementing scalable ETL pipelines and data warehousing solutions across diverse cloud platforms to drive enhanced analytics and business intelligence.
Highlights
Orchestrated end-to-end data pipelines by designing and implementing robust ETL/ELT solutions using Azure Data Factory (ADF), Databricks (PySpark, DLT), and DBT, processing high-volume data from diverse sources (SQL Server, ADLS, volumes) into Delta Lake and Unity Catalog, ensuring data quality and accessibility.
Optimized data performance and architecture by applying Spark optimization techniques (auto compact, auto optimize) and leveraging Delta Lake partitioning strategies, significantly enhancing query performance and reducing storage costs; automated incremental ingestion via DLT pipelines and Spark Structured Streaming.
Pioneered Microsoft Fabric adoption by gaining hands-on expertise and developing a real-time medallion architecture project, integrating ADF, PySpark, and Dataflow Gen 2 to load, transform, and cleanse data into Lakehouse tables, and created SQL Semantic Models for Power BI integration.
Data Engineer (Project)
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Summary
Led data engineering efforts for an automotive project, processing sensor, signal, and CAN data using a Lakehouse Architecture to enable advanced analytics and visualization.
Highlights
Architected and implemented a Lakehouse solution for automotive sensor, signal, and CAN data, processing raw mf4 files into structured formats (Parquet/CSV) in ADLS using Azure Data Factory.
Performed complex aggregate calculations on data stored in Hive Metastore tables and loaded refined, aggregated datasets into Delta tables to support advanced analytical requirements.
Developed interactive visualization dashboards using Plotly to analyze data in Delta tables and leveraged KQL for real-time aggregation and visualization of large streaming data within Azure Data Explorer (ADX).
Data Engineer (Project)
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Summary
Managed end-to-end data processing for a retail project, implementing a Medallion Architecture to support incremental and historical data loads for Power BI reporting.
Highlights
Implemented a Medallion Architecture for a retail data project, ensuring robust processing of daily incremental and historical loads for critical business data (invoices, transactions, timestamps).
Developed and deployed Azure Databricks notebooks, leveraging wide transformation methods to meticulously cleanse and refine raw data, moving it from the Bronze to Silver layer.
Orchestrated data flow into the final Gold layer using Azure Data Factory and Azure Synapse pipelines, successfully enabling direct connectivity for Power BI, delivering critical reports to clients.
Data Engineer Intern
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Summary
Gained foundational experience in data engineering and business intelligence as an intern, developing dashboards and mini-projects using SQL, Power BI, and cloud concepts.
Highlights
Acquired foundational knowledge in SQL, Business Intelligence, and cloud platforms (Azure, AWS) to support data engineering initiatives.
Developed Power BI dashboards by connecting to SQL and BI servers, culminating in a mini-project that showcased dashboard creation skills.
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Bachelor of Science
Computer Science
SQL, PySpark, KQL, Python.
Azure Databricks, Azure Data Factory (ADF), Azure Data Lake Storage (ADLS), Unity Catalog, Delta Lake, Microsoft Fabric, Fabric Lakehouse, Fabric Warehouse, Azure Data Explorer (ADX), Azure Synapse.
ETL, ELT, Data Warehousing, Data Modeling, DBT (Data Build Tool), Delta Live Tables (DLT), Spark Optimization, Metadata-driven ETL.
Power BI, SQL Semantic Models, Data Visualization, Plotly.
Medallion Architecture, Lakehouse Architecture, CI/CD, Databricks Asset Bundles, Jinja Templating, SQL Server, Oracle DB, SFTP, APIs, Azure Key Vault.
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Summary
A data integration project for water and wastewater services in the Northwest of England, focusing on handling and processing data related to water bills and meter readings from multiple sources (SFTP, APIs, Oracle DB, and file-based systems).