Pranav Prasanth
Croydon
Summary
Accomplished Data Engineer with 3.5 years of experience in designing and maintaining data solutions using Microsoft Azure Data Services. Expertise in developing efficient data pipelines, implementing ETL processes, and ensuring data quality for seamless data migration. Proven ability to deliver impactful, data-driven solutions while collaborating with cross-functional teams.
Overview
4
4
years of professional experience
1
1
Certification
Work History
Data Engineer
Alignerr.com
08.2024 - Current
- Ensured high-quality AI training datasets by preparing, cleaning, and structuring large datasets.
- Utilized Python, SQL, and PySpark to build robust ETL-style data processing pipelines for efficiency.
- Developed SQL scripts for data profiling, quality checks, and deduplication to enhance data integrity.
- Processed medium-to-large datasets using PySpark, enabling distributed transformations and reusable data processing modules.
- Designed and maintained scalable database solutions for analytical purposes.
- Designed comprehensive data transformation logic for enrichment, normalization, and feature extraction processes.
- Automated repetitive data tasks with Python, significantly reducing manual workload and improving efficiency by 20%.
- Conducted thorough anomaly detection and validation checks to uphold dataset quality for machine learning.
- Collaborated closely with senior engineers to understand data requirements and deliver structured, analysis-ready datasets.
- Developed data pipelines to facilitate efficient data collection and processing.
- Optimized ETL processes to improve data accuracy and accessibility.
- Implemented automated testing procedures to enhance data quality assurance.
Azure Data Engineer (Intern)
Rabux
01.2022 - 05.2023
- Facilitated efficient data ingestion into Azure services, enhancing data accessibility and analytics capabilities.
- Designed and implemented ETL processes using Azure Data Factory for robust data management.
- Developed and maintained data pipelines in Azure Data Factory, ensuring seamless integration from various sources.
- Optimized Spark applications in Databricks, driving valuable insights into customer usage patterns.
- Authored efficient SQL queries, aligning solutions with technical specifications and business goals.
- Collaborated with stakeholders to identify data challenges, escalating critical issues for timely resolution.
- Partnered with reporting teams to deliver customized datasets for insightful decision-making processes.
- Conducted comprehensive testing and validation of ETL processes, ensuring data accuracy and reliability.
- Developed ETL pipelines to streamline data processing and enhance reporting efficiency by 10%
- Collaborated with data analysts to identify requirements for data models and visualizations
Education
Master of Science - Engineering Management
UNIVERSITY OF GREENWICH
London, United Kingdom01.2023
Bachelor of Science - Computer Engineering
PSN COLLEGE OF ENGINEERING AND TECHNOLOGY
India01.2017
Skills
- Azure Data Factory (ADF)
- Databricks
- ADLS Gen 2
- Azure Synapse
- Microsoft Fabric
- REST API
- Azure DevOps
- Python
- Spark framework
- SQL
- Azure SQL DB
- SQL server
- Power BI
- Machine Learning
- NLP
- Gen AI
- Unit Testing
- Data Integration
Certification
- Databricks Data Engineer Associate, 11/01/25
- Azure Data Fundamental (DP-900), 08/01/25
- Databricks Lake House Fundamentals, 05/01/24
- Databricks Generative AI Skills Bootcamp in Data Science, 05/01/24
Timeline
Data Engineer
Alignerr.com
08.2024 - Current
Azure Data Engineer (Intern)
Rabux
01.2022 - 05.2023
Master of Science - Engineering Management
UNIVERSITY OF GREENWICH
Bachelor of Science - Computer Engineering
PSN COLLEGE OF ENGINEERING AND TECHNOLOGY
Project
- Built end-to-end Azure pipelines processing 1M+ records daily using ADF, Databricks, ADLS Gen2, and Key Vault.
- Extracted, transformed, and loaded data from APIs, CSVs, and relational sources.
- Migrated on-premises SQL Server data to Azure SQL and ADLS using ADF and Self Hosted Integration Runtime.
- Created metadata-driven pipelines with stored procedures for automation.
- Reduced manual processing by 50 hours/month through automated pipelines.
- Optimized Delta Lake tables, improving query performance by 40%. Implemented secure CI/CD pipelines via Azure DevOps.