Aakash Jeyachandran
Data Engineer | Data Analyst • j***********@gmail.com • 217****768 • linkedin.com/•••••
Professional Summary
Data engineer with 5+ years building and operating cloud-native data platforms and end-to-end ETL/ELT pipelines. Proven experience designing and deploying Azure Data Factory pipelines, cloud data lakes, Databricks transformations, and automation using Python and DevOps practices. Delivered scalable solutions across Azure and AWS to improve pipeline throughput, reduce runtimes, and enable self-service analytics for business stakeholders. Strong collaborator who partners with engineering and analytics teams to drive reliability, monitoring, and production SLAs.
Technical Skills
Programming Languages: Python,PowerShell,JavaScript
Frameworks and Libraries: Pandas,NumPy,Scikit-learn
Databases: SQL,MySQL,PostgreSQL,Snowflake,BigQuery,MongoDB
Cloud and DevOps: Azure Data Factory,Azure Synapse,Azure Databricks,Azure SQL,Azure Storage,Azure Key Vault,AWS S3,AWS EC2,AWS Glue,Azure DevOps,YAML pipelines,GitHub Actions,ARM templates,Jenkins,Linux
Data and Analytics: ETL,ELT pipeline design,Data lake architecture,Data transformation,Airflow,SSIS,Power BI,Tableau,Amazon QuickSight,Grafana
Tools and Methodologies: Git,Jira
Monitoring & Logging: Azure Monitor,Log Analytics,Splunk
Collaboration & Process: Agile Scrum,JFrog
Operating Systems: Windows
Work Experience
Lereta
Data Engineer
Nov 2025 – Mar 2026
Tech Stack: Azure Data Factory, Azure Data Lake, Azure SQL, Python, Linux, Azure Monitor, Log Analytics
- Designed and deployed scalable Azure Data Factory pipelines to ingest batch and streaming sources into Azure Data Lake and Azure SQL, enabling analytics teams to access standardized, analytics-ready datasets and improving pipeline throughput by 35%.
- Migrated legacy on-prem ETL workflows to cloud-native ADF and Azure SQL architecture, removing infrastructure bottlenecks and reducing end-to-end job runtime by 40%.
- Developed Python-based transformation modules and reusable ADF pipeline templates to standardize logic and accelerate development, increasing processing speed by 30%.
- Implemented Linux-based execution and monitoring procedures and integrated Azure Monitor and Log Analytics to detect failures and maintain SLA compliance for production workloads.
- Performed proactive monitoring, troubleshooting, and optimization of production data pipelines to reduce runtime variability and ensure high reliability and SLA adherence.
- Collaborated with engineering and analytics teams to integrate downstream consumers, define data contracts, and operationalize CI/CD for pipeline deployments to improve release consistency.
AIKNOD
AZURE DEVOPS Engineer | Cloud & Data Engineer
Jun 2025 – Nov 2025
Tech Stack: Azure DevOps, Git, YAML pipelines, Python, PowerShell, Azure Monitor, Log Analytics, Azure Key Vault
- Designed and implemented Azure DevOps CI/CD pipelines for data and analytics applications to automate build, validation, and multi-environment deployments.
- Created reusable YAML pipeline templates and deployment scripts in Python and PowerShell to standardize release processes and reduce deployment lead time.
- Established Git-based branching and automated validation workflows to improve code quality and reduce production incidents during releases.
- Implemented secure deployment practices including service connections, Key Vault integration, and pipeline governance to enforce environment and access controls.
- Integrated Azure Monitor and Log Analytics into deployment workflows for automated post-deploy validation, monitoring, and alerting of analytics applications.
- Onboarded applications and engineering teams into Azure DevOps by providing pipeline templates, documentation, and hands-on guidance to ensure compliance with deployment standards.
Genome International
Data Analyst
May 2024 – May 2025
Tech Stack: AWS Glue, Python, SQL, AWS S3, Power BI, Amazon QuickSight, JSON, APIs
- Developed and maintained AWS Glue ETL jobs to process structured and semi-structured healthcare datasets into cloud storage, enabling reliable downstream analytics and reporting.
- Optimized SQL queries and refactored stored procedures to improve data processing performance by ~25%, reducing report generation time.
- Implemented Python-based data validation and cleansing pipelines to improve data quality and accuracy for analytics consumers.
- Built and delivered interactive dashboards using Power BI and Amazon QuickSight to surface actionable insights for business and clinical stakeholders.
- Collaborated with cross-functional teams to gather requirements and translate them into scalable ETL designs and analytics-ready datasets.
- Documented ETL workflows, transformation logic, and reporting pipelines to support maintainability, reproducibility, and operational transparency.
Tata Consultancy Services
Data Analyst
Nov 2021 – Jul 2023
Tech Stack: Azure Data Factory, Azure Synapse, Azure SQL, Power BI, Tableau, Python, SQL
- Designed and maintained Azure-based ETL pipelines for telecom operational data using Azure Data Factory and Azure Synapse to create analytics-ready datasets for reporting.
- Developed interactive Power BI and Tableau dashboards leveraging Synapse and SQL to monitor telecom service and business KPIs for operational teams.
- Implemented data validation and cleansing processes using Python to strengthen data quality and ensure reporting accuracy for stakeholders.
- Modularized and standardized pipeline components to reduce reporting turnaround time by 40% and enable faster delivery of analytics artifacts.
- Partnered with business stakeholders to define transformation rules and ensure dataset outputs aligned with operational and regulatory reporting requirements.
- Mentored junior analysts on ETL best practices, BI development standards, and reusable pipeline patterns to increase team productivity.
Genome International
Analytics Engineer
Dec 2020 – Nov 2021
Tech Stack: AWS S3, AWS EC2, Python, SQL, Power BI, Tableau, Amazon QuickSight, Splunk, Jenkins
- Built and maintained AWS data pipelines using S3 and EC2 to process healthcare structured and semi-structured datasets, improving data availability for analysis.
- Developed interactive dashboards in Power BI, Tableau, and QuickSight to visualize patient trends, operational metrics, and key healthcare KPIs.
- Optimized SQL queries and data transformations to improve processing performance by 25%, enabling faster reporting cycles.
- Implemented Splunk-based log monitoring and anomaly detection to proactively identify pipeline issues and support system reliability.
- Automated data processing and reporting workflows using Python and Jenkins, reducing manual effort by 30% and improving consistency.
- Collaborated with cross-functional clinical and engineering teams to translate healthcare data requirements into actionable analytics solutions.
Log Analytics
Projects
Thyroid Cancer Risk Analysis
Tools Used: Python, Pandas, SQL, Power BI, Tableau, Scikit-learn
- Cleaned and prepared real-world medical datasets using Python and SQL to ensure accuracy, consistency, and reliability for analysis.
- Transformed raw hormone measurements into categorized risk levels and engineered features to improve predictive signal for modeling.
- Built and optimized a Scikit-learn classifier, improving predictive accuracy from 51% to 85% through feature engineering and model tuning.
- Developed interactive Power BI and Tableau dashboards to highlight trends, identify high-risk patient segments, and support data-driven decision making.
- Presented findings with data storytelling techniques to make clinical insights actionable for early diagnosis and care planning.
Bio-Metric Based Multi-Level Authentication System for Smart ATM
Tools Used: Computer Vision, Facial classification, Automation
- Designed facial classification methods to enhance ATM security and reduce fraud cases by 30%.
- Built automation for system updates to improve scalability and system uptime by 25%.
- Integrated multi-level authentication logic to strengthen access control and user verification.
Education
University of Illinois Springfield
Master of Data Analytics • May 2025
SRM TRP Engineering College
Bachelor of Engineering Computer Science • May 2021
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