Kartavya Maheshwari

Associate Data Engineer - Amgen India - Hyderabad, Telengana

(2025-10)

Designed and developed cloud-based data pipelines using Databricks, AWS, PySpark, SQL, and Python to ingest, transform, and curate large-scale research datasets.
Led on-premises to cloud migration of research data, improving data availability, reliability, and performance for downstream analytics and scientific workloads.
Implemented distributed data processing with PySpark to handle high-volume datasets, enabling faster insights for exploratory and analytical research use cases.
Built and optimized ETL/ELT pipelines leveraging Kafka for streaming and near real-time data ingestion.
Applied data quality checks and validation logic to ensure accuracy, consistency, and compliance of research data used in critical decision-making.

Software Engineer 1 - MAQ Software - Noida, Uttar Pradesh

(2023-09 - 2025-10)

Designed and deployed agentic AI frameworks to automate test case generation, execution, validation, and root cause analysis (RCA) using NLP, Playwright, and conversational AI, reducing manual effort by 60%.
Built AI-powered self-service analytics agents to analyze DevOps and DataDog logs, identify production issues, and generate actionable remediation insights.
Developed scalable ETL pipelines using PySpark and SQL, processing 10M+ records daily with 40% performance improvement.
Optimized Azure Synapse and Databricks workloads through partitioning, caching, and cost optimization, reducing cloud spend by 25%.
Designed real-time streaming solutions using Azure Stream Analytics for IoT data with sub-second latency.
Automated data quality validation and metadata management, improving data accuracy by 35%.
Led on-premises SQL Server to Azure Synapse migrations, delivering 99.9% uptime for BI and reporting workloads.
Integrated automated testing pipelines using Playwright and Azure DevOps, reducing manual testing effort and deployment time by 40%.
Implemented AI-driven log analysis and monitoring with DataDog, accelerating root cause detection and incident resolution.
Developed agent-assisted DevOps workflows to auto-generate test cases and bug remediation steps from user stories, improving release velocity and overall code quality.

About